Dissertations / Theses on the topic 'Mixed model varietal selection'
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Paget, Mark Frederick. "Genetic evaluation models and strategies for potato variety selection." Thesis, University of Canterbury. Forestry, 2014. http://hdl.handle.net/10092/9953.
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A series of studies are presented on the genetic evaluation of cultivated potato (Solanum tuberosum L.) to improve the accuracy and efficiency of selection at various stages of a breeding programme. The central theme was the use of correlated data, such as relationship information and spatial and across-trial correlations, within a linear mixed modelling framework to enhance the evaluation of candidate genotypes and to improve the genetic response to selection. Analyses focused on several social and economically-important traits for the enhancement of the nutritional value, disease resistance and yield of potato tubers.
At the formative stages of a breeding scheme, devising a breeding strategy requires an improved understanding of the genetic control of target traits for selection. To guide a strategy that aims to enhance the micronutrient content of potato tubers (biofortification), univariate and multivariate Bayesian models were developed to estimate genetic parameters for micronutrient tuber content from a breeding population generated from crosses between Andean landrace cultivars. The importance of the additive genetic components and extent of the narrow-sense heritability estimates indicated that genotypic 'individual' recurrent selection based on empirical breeding values rather than family-based selection is likely to be the most effective strategy in this breeding population. The magnitude of genetic correlations also indicated that simultaneous increases in important tuber minerals, iron and zinc, could be achieved.
Optimising selection efficiency is an important ambition of plant breeding programmes. Reducing the level of candidate replication in field trials may, under certain circumstances, contribute to this aim. Empirical field data and computer simulations inferred that improved rates of genetic gain with p-rep (partially replicated) testing could be obtained compared with testing in fully replicated trials at the early selection stages, particularly when testing over two locations. P-rep testing was able to increase the intensity of selection and the distribution of candidate entries across locations to account for G×E effects was possible at an earlier stage than is currently practised. On the basis of these results, it was recommended that the full replication of trials (at the first opportunity, when enough planting material is available) at a single location in the early stages of selection should be replaced with the partial replication of selection candidates that are distributed over two locations.
Genetic evaluation aims to identify genotypes with high empirical breeding values (EBVs) for selection as parents. Using mixed models, spatial parameters to target greater control of localised field heterogeneity were estimated and variance models to account for across-trial genetic heterogeneity were tested for the evaluation of soil-borne powdery scab disease and tuber yield traits at the early stages of a selection programme. When spatial effects improved model fit, spatial correlations for rows and columns were mostly small for powdery scab, and often small and negative for marketable and total tuber yield suggesting the presence of interplot competition in some years for tuber yield traits. For the evaluation of powdery scab, genetic variance structures were tested using data from 12 years of long-term potato breeding METs (multi-environment trials). A simple homogeneous correlation model for the genetic effects was preferred over a more complex factor analytic (FA) model. Similarly, for the MET evaluation of tuber yield at the early stages, there was little benefit in using more complex FA models, with simple correlation structures generally the most favourable models fitted. The use of less complex models will be more straightforward for routine implementation of potato genetic evaluations in breeding programmes.
Evaluations for (marketable) tuber yield were extended to multi-location MET data to characterise both genotypes and environments, allowing a re-evaluation of New Zealand MET selection strategies aimed at broad adaptation. Using a factor analytic mixed model, results indicated that the programme’s two main trial locations in the North and the South Islands optimised differentiation between genotypes in terms of G×E effects. There was reasonable performance stability of genotypes across test locations and evidence was presented for some, but limited, genetic progress of cultivars and advanced clonal selections for tuber marketable yield in New Zealand over recent years.
The models and selection strategies investigated and developed in this thesis will allow an improved and more systematic application of genetic evaluations in potato selection schemes. This will provide the basis for well informed decisions to be made on selection candidates for the genetic improvement of potato in breeding programmes.
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Alabiso, Audry. "Linear Mixed Model Selection by Partial Correlation." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587142724497829.
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Abraham, Anita Ann Edwards Lloyd J. "Model selection methods in the linear mixed model for longitudinal data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1859.
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Thesis (DrPH)--University of North Carolina at Chapel Hill, 2008.Title from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Public Health in the Department of Biostatistics, School of Public Health." Discipline: Biostatistics; Department/School: Public Health.
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Yousef, Mohammed A. "Two-Stage SCAD Lasso for Linear Mixed Model Selection." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.
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Lan, Lan. "Variable Selection in Linear Mixed Model for Longitudinal Data." NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-05172006-211924/.
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Fan and Li (JASA, 2001) proposed a family of variable selection procedures for certain parametric models via a nonconcave penalized likelihood approach, where significant variable selection and parameter estimation were done simultaneously, and the procedures were shown to have the oracle property. In this presentation, we extend the nonconcave penalized likelihood approach to linear mixed models for longitudinal data. Two new approaches are proposed to select significant covariates and estimate fixed effect parameters and variance components. In particular, we show the new approaches also possess the oracle property when the tuning parameter is chosen appropriately. We assess the performance of the proposed approaches via simulation and apply the procedures to data from the Multicenter AIDS Cohort Study.
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Wenren, Cheng. "Mixed Model Selection Based on the Conceptual Predictive Statistic." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1403735738.
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Atutey, Olivia Abena. "Linear Mixed Model Selection via Minimum Approximated Information Criterion." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1594910831256966.
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Pan, Juming. "Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.
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Xiong, Jingwei. "A Penalized Approach to Mixed Model Selection Via Cross Validation." Bowling Green State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1510965832174342.
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Ge, Wentao. "Bootstrap-adjusted Quasi-likelihood Information Criteria for Mixed Model Selection." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu156207676645628.
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Ribbing, Jakob. "Covariate Model Building in Nonlinear Mixed Effects Models." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7923.
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Stone, Elizabeth Anne. "Multilevel Model Selection: A Regularization Approach Incorporating Heredity Constraints." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/234414.
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StatisticsPh.D.
This dissertation focuses on estimation and selection methods for a simple linear model with two levels of variation. This model provides a foundation for extensions to more levels. We propose new regularization criteria for model selection, subset selection, and variable selection in this context. Regularization is a penalized-estimation approach that shrinks the estimate and selects variables for structured data. This dissertation introduces a procedure (HM-ALASSO) that extends regularized multilevel-model estimation and selection to enforce principles of fixed heredity (e.g., including main effects when their interactions are included) and random heredity (e.g., including fixed effects when their random terms are included). The goals in developing this method were to create a procedure that provided reasonable estimates of all parameters, adhered to fixed and random heredity principles, resulted in a parsimonious model, was theoretically justifiable, and was able to be implemented and used in available software. The HM-ALASSO incorporates heredity-constrained selection directly into the estimation process. HM-ALASSO is shown to enjoy the properties of consistency, sparsity, and asymptotic normality. The ability of HM-ALASSO to produce quality estimates of the underlying parameters while adhering to heredity principles is demonstrated using simulated data. The performance of HM-ALASSO is illustrated using a subset of the High School and Beyond (HS&B) data set that includes math-achievement outcomes modeled via student- and school-level predictors. The HM-ALASSO framework is flexible enough that it can be adapted for various rule sets and parameterizations.
Temple University--Theses
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Wang, Jun. "Selecting the Best Linear Mixed Model Using Predictive Approaches." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1697.pdf.
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Potter, Douglass W. "A GIS MODEL FOR APIARY SITE SELECTION BASED ON PROXIMITY TO NECTAR SOURCES UTILIZED IN VARIETAL HONEY PRODUCTION ON FORMER MINE SITES IN APPALACHIA." UKnowledge, 2019. https://uknowledge.uky.edu/forestry_etds/46.
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Beekeepers in Appalachia market varietal honeys derived from particular species of deciduous trees; however, finding places in a mountainous landscape to locate new beeyards is difficult. Site selection is hindered by the high up-front costs of negotiating access to remote areas with limited knowledge of the available forage. Remotely sensed data and species distribution modeling (SDM) of trees important to beekeepers could aid in locating apiary sites at the landscape scale. The objectives of this study are i) using publicly available forest inventory data, to model the spatial distribution of three native tree species that are important to honey producers in eastern Kentucky: American Basswood, Sourwood and Tulip Poplar, and to assess the accuracy of the models, ii) to incorporate a method for discounting the value of a nectar resource as a function of distance based on an energetic model of honeybee foraging, and iii) to provide an example by ranking potential apiary locations around the perimeter of a mine site in the study area based on their proximity to probable species habitat using a GIS model.
Logistic regression models were trained using presence-absence records from 1,059 USFS Forest Inventory and Analysis (FIA) sub-plots distributed throughout a 9,000 km2 portion of the Kentucky River watershed. The models were evaluated by applying them to a separate dataset, 950 forest inventory sub-plots distributed over a 40.5 km2 research forest maintained by the University of Kentucky. Weights derived from an energic model of honeybee foraging were then applied to the probabilities of tree species occurrence predicted by the SDM. As an example, 24 potential apiary locations around the perimeter of a reclaimed mine site were selected and then ranked according to a site suitability index. Three tributary areas corresponding to different honeybee flight ranges were considered: 500m, 700m, and 1,200m. Results confirm that rankings are dependent on the foraging range considered, suggesting that the number of colonies at an apiary location would be an important factor to consider when choosing a site. However, the methodology makes assumptions that are only anecdotally supported, notably i) that colonies will forage preferentially at the target species when it is in bloom and, ii) that foragers will exhaust resources closest to the hive first, regardless of patch size. Additional study of how bees deplete the nectar resources surrounding an apiary is needed to verify the usefulness of SDM in site selection for varietal honey production.
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Lee, Yi-Ching. "An Approach to Estimation and Selection in Linear Mixed Models with Missing Data." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1562754262770979.
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Qiu, Chen. "A study of covariance structure selection for split-plot designs analyzed using mixed models." Kansas State University, 2014. http://hdl.handle.net/2097/18129.
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Master of ScienceDepartment of Statistics
Christopher I. Vahl
In the classic split-plot design where whole plots have a completely randomized design, the conventional analysis approach assumes a compound symmetry (CS) covariance structure for the errors of observation. However, often this assumption may not be true. In this report, we examine using different covariance models in PROC MIXED in the SAS system, which are widely used in the repeated measures analysis, to model the covariance structure in the split-plot data in which the simple compound symmetry assumption does not hold. The comparison of the covariance structure models in PROC MIXED and the conventional split-plot model is illustrated through a simulation study. In the example analyzed, the heterogeneous compound symmetry (CSH) covariance model has the smallest values for the Akaike and Schwarz’s Bayesian information criteria fit statistics and is therefore the best model to fit our example data.
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Orelien, Jean Guilmond Edwards Lloyd J. "Use of R2 statistics for assessing goodness-of-fit and model selection in the linear mixed model for longitudinal data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1391.
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Thesis (DrPH)--University of North Carolina at Chapel Hill, 2007.Title from electronic title page (viewed Apr. 25, 2008). "... in partial fulfillment of the requirements for the degree of Doctor in Public Health in the Department of Biostatistics School of Public Health." Discipline: Biostatistics; Department/School: Public Health. On title page, 2 appears in superscript.
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Tüchler, Regina. "Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/984/1/document.pdf.
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The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities, with no additional tuning being needed. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix. For logistic mixed effects models prior determination of explanatory variables and random effects is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the modeling procedure. As an illustration two real-data examples from finance and tourism studies are given. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematics
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Säfken, Benjamin [Verfasser], Thomas [Akademischer Betreuer] Kneib, and Tatyana [Akademischer Betreuer] Krivobokova. "Model choice and variable selection in mixed & semiparametric models / Benjamin Säfken. Gutachter: Thomas Kneib ; Tatyana Krivobokova. Betreuer: Thomas Kneib." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2015. http://d-nb.info/1069664928/34.
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Nuthmann, Antje, Wolfgang Einhäuser, and Immo Schütz. "How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models." Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-232614.
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Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.
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Atwood, Chad Judson. "Effects of Alternative Silvicultural Treatments on Regeneration in the Southern Appalachians." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32997.
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Harvesting practices in the southern Appalachians have moved away from clearcutting in favor of variable retention harvesting systems. A study was initiated in 1995-8 to investigate the effects of retaining varying numbers of residual trees on regeneration in seven silvicultural treatments. A second study specifically focused on stump sprouting in only three of those treatments. The treatments for first study included: a clearcut, commercial harvest, leave-tree, shelterwood, group selection, midstory treatment, and an uncut control. The second only focused on the clearcut, leave-tree, and shelterwood. These treatments were implemented in seven stands in Virginia and West Virginia over two physiographic provinces, the Appalachian plateau and Ridge and Valley. The stands were even-aged oak dominated Appalachian hardwood stands on fair quality sites with average ages ranging from 63 to 100 yrs. Permanent plots were randomly located in each stand and all overstory trees (>5m tall) were inventoried and tagged prior to harvest. Regeneration was also quantified. Harvest occurred between 1995-8. For the current studies the plots were re-inventoried 9-11 years post-harvest and all regeneration in all treatments as well as stump sprouts in the selected treatments were quantified.
The first study utilized a mixed model ANOVA to analyze five species groups: oak, maple, black cherry-yellow-poplar, miscellaneous, and midstory. Response variables included importance value, average height, and density compared within species group and among treatments. Differences between sprout and seedling origin regeneration were also investigated within species group among treatment. Results indicated that oak densities were similar in all of the treatments, and stump sprouts were larger and more frequent than seedlings. Maple exhibited an increase from pre-harvest overstory importance and exhibited competitive sprouting. The black cherry-yellow-poplar group had few but highly competitive sprouts and a considerable increase in seedling origin regeneration in all treatments. The miscellaneous species densities increased as well with more competitive sprouting in some treatments. The midstory species were excluded from the analysis as it was assumed these species would not occupy canopy positions in a mature stand.
The second study investigated differences in the percent of stumps that sprouted and the number of sprouts per stump. The percent data were analyzed using a non-parametric one-way ANOVA and regression analysis, while the sprouts per stump data were compared in a mixed model ANOVA and regression. Species were combined into six groups: the red oak group, chestnut oak, red maple, white oak/hickory group, mixed mesic group, and midstory group. The plateau tended to have reduced sprouting compared to the Ridge and Valley for most species groups and treatments. The red oak group, chestnut oak, and red maple exhibited reduced sprouting with increased residual basal area. The mixed mesic group did not show any effect in sprouting related to residual basal area. Only chestnut oak showed fewer sprouts per stump as residual basal area increased.
Master of Science
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Cole, James Jacob. "Assessing Nonlinear Relationships through Rich Stimulus Sampling in Repeated-Measures Designs." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1587.
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Explaining a phenomenon often requires identification of an underlying relationship between two variables. However, it is common practice in psychological research to sample only a few values of an independent variable. Young, Cole, and Sutherland (2012) showed that this practice can impair model selection in between-subject designs. The current study expands that line of research to within-subjects designs. In two Monte Carlo simulations, model discrimination under systematic sampling of 2, 3, or 4 levels of the IV was compared with that under random uniform sampling and sampling from a Halton sequence. The number of subjects, number of observations per subject, effect size, and between-subject parameter variance in the simulated experiments were also manipulated. Random sampling out-performed the other methods in model discrimination with only small, function-specific costs to parameter estimation. Halton sampling also produced good results but was less consistent. The systematic sampling methods were generally rank-ordered by the number of levels they sampled.
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Margevicius, Seunghee P. "Modeling of High-Dimensional Clinical Longitudinal Oxygenation Data from Retinopathy of Prematurity." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523022165691473.
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Guan, Youliang. "Crack path selection and shear toughening effects due to mixed mode loading and varied surface properties in beam-like adhesively bonded joints." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/24905.
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Structural adhesives are widely used with great success, and yet occasional failures can occur, often resulting from improper bonding procedures or joint design, overload or other detrimental service situations, or in response to a variety of environmental challenges. In these situations, cracks can start within the adhesive layer or debonds can initiate near an interface. The paths taken by propagating cracks can affect the resistance to failure and the subsequent service lives of the bonded structures. The behavior of propagating cracks in adhesive joints remains of interest, including when some critical environments, complicated loading modes, or uncertainties in material/interfacial properties are involved. From a mechanics perspective, areas of current interest include understanding the growth of damage and cracks, loading rate dependency of crack propagation, and the effect of mixed mode fracture loading scenarios on crack path selection. This dissertation involves analytical, numerical, and experimental evaluations of crack propagation in several adhesive joint configurations. The main objective is an investigation of crack path selection in adhesively bonded joints, focusing on in-plane fracture behavior (mode I, mode II, and their combination) of bonded joints with uniform bonding, and those with locally weakened interfaces.
When removing cured components from molds, interfacial debonds can sometimes initiate and propagate along both mold surfaces, resulting in the molded product partially bridging between the two molds and potentially being damaged or torn. Debonds from both adherends can sometimes occur in weak adhesive bonds as well, potentially altering the apparent fracture behavior. To avoid or control these multiple interfacial debonding, more understanding of these processes is required. An analytical model of 2D parallel bridging was developed and the interactions of interfacial debonds were investigated using Euler-Bernoulli beam theory. The numerical solutions to the analytical results described the propagation processes with multiple debonds, and demonstrated some common phenomena in several different joints corresponding to double cantilever beam configurations. The analytical approach and results obtained could prove useful in extensions to understanding and controlling debonding in such situations and optimization of loading scenarios.
Numerical capabilities for predicting crack propagation, confirmed by experimental results, were initially evaluated for crack behavior in monolithic materials, which is also of interest in engineering design. Several test cases were devised for modified forms of monolithic compact tension specimens (CT) were developed. An asymmetric variant of the CT configuration, in which the initial crack was shifted to two thirds of the total height, was tested experimentally and numerically simulated in ABAQUS®, with good agreement. Similar studies of elongated CT specimens with different specimen lengths also revealed good agreement, using the same material properties and cohesive zone model (CZM) parameters. The critical specimen length when the crack propagation pattern abruptly switches was experimentally measured and accurately predicted, building confidence in the subsequent studies where the numerical method was applied to bonded joints.
In adhesively bonded joints, crack propagation and joint failure can potentially result from or involve interactions of a growing crack with a partially weakened interface, so numerical simulations were initiated to investigate such scenarios using ABAQUS®. Two different cohesive zone models (CZMs) are applied in these simulations: cohesive elements for strong and weak interfaces, and the extended finite element method (XFEM) for cracks propagating within the adhesive layer. When the main crack approaches a locally weakened interface, interfacial damage can occur, allowing for additional interfacial compliance and inducing shear stresses within the adhesive layer that direct the growing crack toward the weak interface. The maximum traction of the interfacial CZM appears to be the controlling parameter. Fracture energy of the weakened interface is shown to be of secondary importance, though can affect the results when particularly small (e.g. 1% that of the bulk adhesive). The length of the weakened interface also has some influence on the crack path. Under globally mixed mode loadings, the competition between the loading and the weakened interface affects the shear stress distribution and thus changes the crack path. Mixed mode loading in the opposite direction of the weakened interface is able to drive the crack away from the weakened interface, suggesting potential means to avoid failure within these regions or to design joints that fail in a particular manner.
In addition to the analytical and numerical studies of crack path selection in adhesively bonded joints, experimental investigations are also performed. A dual actuator load frame (DALF) is used to test beam-like bonded joints in various mode mixity angles. Constant mode mixity angle tracking, as well as other versatile loading functions, are developed in LabVIEW® for use with a new controller system. The DALF is calibrated to minimize errors when calculating the compliance of beam-like bonded joints. After the corrections, the resulting fracture energies ( ) values are considered to be more accurate in representing the energy released in the crack propagation processes. Double cantilever beam (DCB) bonded joints consisting of 6061-T6 aluminum adherends bonded with commercial epoxy adhesives (J-B Weld, or LORD 320/322) are tested on the DALF. Profiles of the values for different constant mode mixity angles, as well as for continuously increasing mode mixity angle, are plotted to illustrate the behavior of the crack in these bonded joints.
Finally, crack path selection in DCB specimens with one of the bonding surfaces weakened was studied experimentally, and rate-dependency of the crack path selection was found. Several contamination schemes are attempted, involving of graphite flakes, silicone tapes, or silane treatments on the aluminum oxide interfaces. In all these cases, tests involving more rapid crack propagation resulted in interfacial failures at the weakened areas, while slower tests showed cohesive failure throughout. One possible explanation of this phenomenon is presented using the rate-dependency of the yield stress (commonly considered to be corresponding to the maximum traction) of the epoxy adhesives. These experimental observations may have some potential applications tailoring adhesive joint configurations and interface variability to achieve or avoid particular failure modes.Ph. D.
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Yamanouchi, Tatiana Kazue. "Seleção de modelos lineares mistos utilizando critérios de informação." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-08032018-131129/.
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O modelo misto é comumente utilizado em dados de medidas repetidas devido a sua flexibilidade de incorporar no modelo a correlação existente entre as observações medidas no mesmo indivíduo e a heterogeneidade de variâncias das observações feitas ao longo do tempo. Este modelo é composto de efeitos fixos, efeitos aleatórios e o erro aleatório e com isso na seleção do modelo misto muitas vezes é necessário selecionar os melhores componentes do modelo misto de tal forma que represente bem os dados. Os critérios de informação são ferramentas muito utilizadas na seleção de modelos, mas não há muitos estudos que indiquem como os critérios de informação se desempenham na seleção dos efeitos fixos, efeitos aleatórios e da estrutura de covariância que compõe o erro aleatório. Diante disso, neste trabalho realizou-se um estudo de simulação para avaliar o desempenho dos critérios de informação AIC, BIC e KIC na seleção dos componentes do modelo misto, medido pela taxa TP (Taxa de verdadeiro positivo). De modo geral, os critérios de informação se desempenharam bem, ou seja, tiveram altos valores de taxa TP em situações em que o tamanho da amostra é maior. Na seleção de efeitos fixos e na seleção da estrutura de covariância, em quase todas as situações, o critério BIC teve um desempenho melhor em relação aos critérios AIC e KIC. Na seleção de efeitos aleatórios nenhum critério teve um bom desempenho, exceto na seleção de efeitos aleatórios em que considera a estrutura de simetria composta, situação em que BIC teve o melhor desempenho.The mixed model is commonly used in data of repeated measurements because of its flexibility to incorporate in the model the correlation existing between the observations measured in the same individual and the heterogeneity of variances of observations made over time. This model is composed of fixed effects, random effects and random error and with this in the selection of the mixed model it is often necessary to select the best components of the mixed model in such a way that it represents the data well. Information criteria are tools widely used in model selection, but there are not many studies that indicate how information criteria play out in the selection of fixed effects, random effects, and the covariance structure that makes up the random error. In this work, a simulation study was performed to evaluate the performance of the AIC, BIC and KIC information criteria in the selection of the components of the mixed model, measured by the TP (True positive Rate). In general, the information criteria performed well, that is, they had high TP rate in situations where the sample size is larger. In the selection of fixed effects and in the selection of the covariance structure, in almost all situations, the BIC criterion had a better performance in relation to the AIC and KIC criteria. In the selection of random effects no criterion had a good performance, except in the selection of Random effects in which it considers the compound symmetric structure, situation in which BIC had the best performance.
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Huo, Shuning. "Bayesian Modeling of Complex High-Dimensional Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.
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With the rapid development of modern high-throughput technologies, scientists can now
collect high-dimensional complex data in different forms, such as medical images, genomics
measurements. However, acquisition of more data does not automatically lead to better
knowledge discovery. One needs efficient and reliable analytical tools to extract useful information
from complex datasets. The main objective of this dissertation is to develop innovative
Bayesian methodologies to enable effective and efficient knowledge discovery from
complex high-dimensional data. It contains two parts—the development of computationally
efficient functional mixed models and the modeling of data heterogeneity via Dirichlet
Diffusion Tree. The first part focuses on tackling the computational bottleneck in Bayesian
functional mixed models. We propose a computational framework called variational functional
mixed model (VFMM). This new method facilitates efficient data compression and
high-performance computing in basis space. We also propose a new multiple testing procedure
in basis space, which can be used to detect significant local regions. The effectiveness
of the proposed model is demonstrated through two datasets, a mass spectrometry dataset
in a cancer study and a neuroimaging dataset in an Alzheimer's disease study. The second
part is about modeling data heterogeneity by using Dirichlet Diffusion Trees. We propose
a Bayesian latent tree model that incorporates covariates of subjects to characterize the
heterogeneity and uncover the latent tree structure underlying data. This innovative model
may reveal the hierarchical evolution process through branch structures and estimate systematic
differences between groups of samples. We demonstrate the effectiveness of the model
through the simulation study and a brain tumor real data.Doctor of Philosophy
With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional data in different forms, such as engineering signals, medical images, and genomics measurements. However, acquisition of such data does not automatically lead to efficient knowledge discovery. The main objective of this dissertation is to develop novel Bayesian methods to extract useful knowledge from complex high-dimensional data. It has two parts—the development of an ultra-fast functional mixed model and the modeling of data heterogeneity via Dirichlet Diffusion Trees. The first part focuses on developing approximate Bayesian methods in functional mixed models to estimate parameters and detect significant regions. Two datasets demonstrate the effectiveness of proposed method—a mass spectrometry dataset in a cancer study and a neuroimaging dataset in an Alzheimer's disease study. The second part focuses on modeling data heterogeneity via Dirichlet Diffusion Trees. The method helps uncover the underlying hierarchical tree structures and estimate systematic differences between the group of samples. We demonstrate the effectiveness of the method through the brain tumor imaging data.
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Sobreira, Fábio Moreira. "Melhor predição linear não viesada (BLUP) multicaracterística na seleção recorrente de plantas anuais." Universidade Federal de Viçosa, 2009. http://locus.ufv.br/handle/123456789/4696.
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The BLUP methodology, which is widely used in animal and forestry genetic evaluation, can also be applied to annual crop breeding. The objective of this study was to compare the accuracy and efficiency of among- and within-half-sib family selection through the use of multi-trait BLUP, single-trait BLUP and phenotypic selection. Expansion volume and yield data from two recurrent selection cycles of a popcorn population were analyzed. Progeny tests were designed as a lattice. In order to maximize accuracy of the prediction of breeding values, the BLUP analyses included phenotypic values of the two cycles. All statistical analyses were performed using the ASREML software. The multi-trait BLUP method demonstrated greater accuracy and efficiency in family selection. In the case of within-family selection, both accuracy and efficiency of multi-trait or single-trait BLUP methods were equivalent. The selection efficiency of the multi-trait BLUP was dependent on the estimated genetic parameters, particularly the difference between the genetic and environmental correlations of the traits.
A metodologia BLUP, que é amplamente utilizada na avaliação genética animal e florestal também pode ser aplicada no melhoramento de culturas anuais. O objetivo deste estudo foi comparar a acurácia e a eficiência da seleção entre e dentro de famílias de meios-irmãos através da utilização do BLUP multicaracterística, BLUP unicaracterística e seleção fenotípica. Dados de capacidade de expansão e produção de dois ciclos de seleção recorrente em uma população de milho-pipoca foram analisados. Os testes de progênies foram delineados como um látice. Visando maximizar a acurácia da predição dos valores genéticos as análises BLUP incluíram valores fenotípicos dos dois ciclos. Todas as análises estatísticas foram realizadas utilizando o software ASREML. O método BLUP multicaracterística apresentou maior acurácia e eficiência de seleção de famílias. No caso da seleção dentro de famílias a acurácia e a eficiência dos métodos BLUP multicaracterística e BLUP unicaracterística foram equivalentes. A eficiência de seleção do BLUP multicaracterística foi dependente dos parâmetros genéticos estimados, particularmente da diferença entre as correlações genéticas e ambientais das características.
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Milet, Jacqueline. "Étude de la composante génétique de la variabilité des infections palustres simples : Approche génome entier dans deux cohortes de jeunes enfants au Bénin First Genome-Wide Association Study of Non-Severe Malaria in Two Birth Cohorts in Benin Mixed logistic regression in Genome-Wide Association Studies." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASR013.
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Malgré les moyens importants de prévention et de lutte mis en place ces dernières années, le paludisme reste dévastateur avec près d’un demi-million de décès par an (405 000 en 2018, d'après le dernier rapport de l'OMS). Le rôle clé joué par les facteurs génétiques de l’hôte dans la susceptibilité et la sévérité de la maladie est admis aujourd'hui. Cependant, les bases moléculaires de la sensibilité/résistance au paludisme restent encore mal connues. Ces dix dernières années, les efforts de recherches pour l’identification de gènes impliqués dans la sensibilité au paludisme à P. falciparum se sont concentrés sur les formes graves de paludisme, avec plusieurs plusieurs études d’association sur l’ensemble du génome (Genome-Wide Association Study ou GWAS) publiées. Ce manuscrit porte sur l’extension de cette approche aux formes simples du paludisme, au travers de l’étude d’association génome entier de deux cohortes de nouveau-nés au Sud Bénin (au total 800 enfants), suivis pendant 18-24 mois par l’UMR261 (MERIT IRD/Université de Paris). Dans une première partie nous présentons les résultats de la première GWAS réalisée sur les formes simples de paludisme dans ces deux cohortes. L’association a été testée avec la récurrence des accès palustres et la récurrence de l’ensemble des infections (incluant les accès palustres et les infections asymptomatiques) en prenant en compte un risque environnemental estimé au niveau individuel. Elle met en évidence plusieurs signaux d’association forts, en lien avec des gènes dont la fonction biologique est pertinente pour le paludisme (notamment PTPRT, MYLK4, UROC1 et ACER3). La forte variabilité génétique présente au sein des populations africaines a nécessité de prendre en compte l’effet de confusion potentiel de la structure de population. Dans l’étude les formes simples de paludisme, une approche en deux étapes a été utilisée, le modèle de Cox mixte, utilisé pour l’analyse des données longitudinales, n’étant pas applicable à l’ensemble du génome du fait du temps de calcul nécessaire. Un modèle de Cox mixte a été appliqué pour construire un « effet individuel » ajusté sur les covariables, puis un modèle mixte linéaire pour tester l’association avec les polymorphismes du génome. Ceci nous a conduits à nous intéresser plus généralement aux modèles mixtes non-linéaires. Deux méthodes permettant l’estimation de l’effet des polymorphismes avec le modèle logistique mixte sont proposées, qui pourront être dans le futur généralisé à d’autres modèles, dont le modèle de Cox. Dans une dernière partie, le paludisme ayant constitué une des plus fortes pressions de sélection que l’homme ait connue dans son histoire récente, nous explorons la possibilité d’exploiter l’information de sélection naturelle pour augmenter la puissance de l’analyse, et améliorer la détection des signaux d’association. L’analyse des signaux de sélection positive récente sur l’ensemble du génome a été réalisée avec plusieurs méthodes basées sur les haplotypes longs ((iHS, nsL and XP-EHH). Celle-ci met en évidence plusieurs régions chromosomiques d’intérêt potentiel où les signaux d’association et de sélection co-localisent ; mais confirme également la difficulté à mettre en évidence les signaux de sélection liés au paludisme avec les outils disponibles actuellementIn spite of numerous prevention and control efforts in recent years, malaria remains a major global public health problem with nearly half a million deaths per year (405,000 in 2018). The key role played by genetic factors of the host in the susceptibility and severity of the disease is is admitted nowadays. However, the molecular basis of susceptibility / resistance to malaria has not been elucidated to date. Over the past decade, research efforts to identify genes involved in malaria susceptibility have focused on severe malaria, with several genome-wide association studies (GWAS) published. This manuscript concerns the extension of this approach to uncomplicated forms of malaria, through the genome wide association study of two birth cohorts in South Benin (800 children), followed for 18-24 months by UMR261 (MERIT IRD / University of Paris).In the first part, we present the results of the first GWAS performed on simple forms of malaria in these two cohorts. The association was tested with the recurrence of malaria attacks and the recurrence of all infections (including malaria attacks and asymptomatic infections) taking into account an environmental risk estimated at the individual level. It highlights several strong association signals, linked to genes whose biological function is relevant for malaria (in particular PTPRT, MYLK4, UROC1 and ACER3). The high genetic diversity within African populations has made it necessary to take into account the potential confounding effect of population structure. In this study we proceeded with a two-step strategy as the Cox mixed model, used for the analysis of longitudinal data, is not applicable to the whole genome due to computational burden. In a first step, an analysis was performed with a Cox mixed model to build an "individual effect" fitted on the covariates, then a linear mixed model were used to test the association with genome polymorphisms. This led us to focus more generally on non-linear mixed models. Two methods allowing the estimation of the effect of polymorphisms with the mixed logistic model are proposed, which may in the future be generalized to other models, including the Cox model.In a final part, malaria having been one of the strongest selection pressures that man has known in recent history, we explore the possibility of exploiting natural selection information to increase the power of analysis, and improve the detection of association signals. The analysis of recent positive selection signals were performed using several genome-scan methods focusing on patterns of long-range haplotype homozygosity (iHS, nsL and XP-EHH). This analysis revealed several chromosomic region of potential interest, where the signals of association and selection co-localized but confirms also the difficulty of highlighting the selection signals linked to malaria with tools currently available
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Shen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.
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This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision. Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes. The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).
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Schneider, Rhiannon N. "Genome-Wide Analyses for Partial Resistance to Phytophthora sojae Kaufmann and Gerdemann in Soybean (Glycine max L. Merr.) Populations from North America and the Republic of Korea." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429741967.
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Buatois, Simon. "Novel pharmacometric methods to improve clinical drug development in progressive diseases." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC133.
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Suite aux progrès techniques et méthodologiques dans le secteur de la modélisation, l’apport de ces approches est désormais reconnu par l’ensemble des acteurs de la recherche clinique et pourrait avoir un rôle clé dans la recherche sur les maladies progressives. Parmi celles-ci les études pharmacométriques (PMX) sont rarement utilisées pour répondre aux hypothèses posées dans le cadre d’études dites de confirmation. Parmi les raisons évoquées, les analyses PMX traditionnelles ignorent l'incertitude associée à la structure du modèle lors de la génération d'inférence statistique. Or, ignorer l’étape de sélection du modèle peut aboutir à des intervalles de confiance trop optimistes et à une inflation de l’erreur de type I. Pour y remédier, nous avons étudié l’apport d’approches PMX innovantes dans les études de choix de dose. Le « model averaging » couplée à un test du rapport de « vraisemblance combiné » a montré des résultats prometteurs et tend à promouvoir l’utilisation de la PMX dans les études de choix de dose. Pour les études dites d’apprentissage, les approches de modélisation sont utilisées pour accroitre les connaissances associées aux médicaments, aux mécanismes et aux maladies. Dans cette thèse, les mérites de l’analyse PMX ont été évalués dans le cadre de la maladie de Parkinson. En combinant la théorie des réponses aux items à un modèle longitudinal, l’analyse PMX a permis de caractériser adéquatement la progression de la maladie tout en tenant compte de la nature composite du biomarqueur. Pour conclure, cette thèse propose des méthodes d’analyses PMX innovantes pour faciliter le développement des médicaments et/ou les décisions des autorités réglementairesIn the mid-1990, model-based approaches were mainly used as supporting tools for drug development. Restricted to the “rescue mode” in situations of drug development failure, the impact of model-based approaches was relatively limited. Nowadays, the merits of these approaches are widely recognised by stakeholders in healthcare and have a crucial role in drug development for progressive diseases. Despite their numerous advantages, model-based approaches present important drawbacks limiting their use in confirmatory trials. Traditional pharmacometric (PMX) analyses relies on model selection, and consequently ignores model structure uncertainty when generating statistical inference. The problem of model selection is potentially leading to over-optimistic confidence intervals and resulting in a type I error inflation. Two projects of this thesis aimed at investigating the value of innovative PMX approaches to address part of these shortcomings in a hypothetical dose-finding study for a progressive disorder. The model averaging approach coupled to a combined likelihood ratio test showed promising results and represents an additional step towards the use of PMX for primary analysis in dose-finding studies. In the learning phase, PMX is a key discipline with applications at every stage of drug development to gain insight into drug, mechanism and disease characteristics with the ultimate goal to aid efficient drug development. In this thesis, the merits of PMX analysis were evaluated, in the context of Parkinson’s disease. An item-response theory longitudinal model was successfully developed to precisely describe the disease progression of Parkinson’s disease patients while acknowledging the composite nature of a patient-reported outcome. To conclude, this thesis enhances the use of PMX to aid efficient drug development and/or regulatory decisions in drug development
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Ollier, Edouard. "Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEN097.
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Cette thèse est principalement consacrée au développement de méthodes de sélection de modèles par maximum de vraisemblance pénalisée dans le cadre de données complexes. Un premier travail porte sur la sélection des modèles linéaires généralisés dans le cadre de données stratifiées, caractérisées par la mesure d’observations ainsi que de covariables au sein de différents groupes (ou strates). Le but de l’analyse est alors de déterminer quelles covariables influencent de façon globale (quelque soit la strate) les observations mais aussi d’évaluer l’hétérogénéité de cet effet à travers les strates.Nous nous intéressons par la suite à la sélection des modèles non linéaires à effets mixtes utilisés dans l’analyse de données longitudinales comme celles rencontrées en pharmacocinétique de population. Dans un premier travail, nous décrivons un algorithme de type SAEM au sein duquel la pénalité est prise en compte lors de l’étape M en résolvant un problème de régression pénalisé à chaque itération. Dans un second travail, en s’inspirant des algorithmes de type gradient proximaux, nous simplifions l’étape M de l’algorithme SAEM pénalisé précédemment décrit en ne réalisant qu’une itération gradient proximale à chaque itération. Cet algorithme, baptisé Stochastic Approximation Proximal Gradient algorithm (SAPG), correspond à un algorithme gradient proximal dans lequel le gradient de la vraisemblance est approché par une technique d’approximation stochastique.Pour finir, nous présentons deux travaux de modélisation statistique, réalisés au cours de cette thèseThis thesis is mainly devoted to the development of penalized maximum likelihood methods for the study of complex data.A first work deals with the selection of generalized linear models in the framework of stratified data, characterized by the measurement of observations as well as covariates within different groups (or strata). The purpose of the analysis is then to determine which covariates influence in a global way (whatever the stratum) the observations but also to evaluate the heterogeneity of this effect across the strata.Secondly, we are interested in the selection of nonlinear mixed effects models used in the analysis of longitudinal data. In a first work, we describe a SAEM-type algorithm in which the penalty is taken into account during step M by solving a penalized regression problem at each iteration. In a second work, inspired by proximal gradient algorithms, we simplify the M step of the penalized SAEM algorithm previously described by performing only one proximal gradient iteration at each iteration. This algorithm, called Stochastic Approximation Proximal Gradient Algorithm (SAPG), corresponds to a proximal gradient algorithm in which the gradient of the likelihood is approximated by a stochastic approximation technique.Finally, we present two statistical modeling works realized during this thesis
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Oesterle, Jonathan. "Holistic approach to designing hybrid assembly lines A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives Evaluation of the influence of dominance rules for the assembly line design problem under consideration of product design alternatives Hybrid Multi-objective Optimization Method for Solving Simultaneously the line Balancing, Equipment and Buffer Sizing Problems for Hybrid Assembly Systems Comparison of Multiobjective Algorithms for the Assembly Line Balancing Design Problem Efficient multi-objective optimization method for the mixed-model-line assembly line design problem Detaillierungsgrad von Simulationsmodellen Rechnergestützte Austaktung einer Mixed-Model Line. Der Weg zur optimalen Austaktung." Thesis, Troyes, 2017. http://www.theses.fr/2017TROY0012.
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Le travail présenté dans cette thèse concerne la formulation et la résolution de deux problèmes d'optimisation multi-objectifs. Ces problèmes de décision, liés à une approche holistique, ont pour but de sélectionner la meilleure configuration « produit/ligne d’assemblage » à partir d'un ensemble de design produits, et de ressources. Concernant le premier problème, un modèle de coût a été développé afin de traduire les interdépendances complexes entre la sélection d’un design produit et les caractéristiques des ressources. Une étude empirique est proposée et vise à comparer, selon plusieurs indicateurs de qualité multi-objectifs, différentes méthodes de résolution - comprenant des algorithmes génétiques, de colonies de fourmis, d’optimisation par essaims particulaires, des chauves-souris, de recherche du coucou et de pollinisation des fleurs. Plusieurs règles de dominance et une recherche locale spécifique au problème ont été appliquées aux méthodes de résolution les plus prometteuses. Concernant le second problème, qui se penche également sur le dimensionnement des stocks tampons, les méthodes de résolution sont à un modèle de simulation à événements discrets, dont la fonction première est l’évaluation des valeurs des différentes fonctions objectives. L’approche holistique associée aux deux problèmes a été validée avec deux cas industrielsThe work presented in this thesis concerns the formulation and the resolution of two holistic multi-objective optimization problems associated with the selection of the best product and hybrid assembly line configuration out of a set of products, processes and resources alternatives. Regarding the first problem, a cost model was developed in order to translate the complex interdependencies between the selection of specific product designs, processes and resources characteristics. An empirical study is proposed, which aimed at comparing, according to several multi-objective quality indicators, various resolution methods – including variants of evolutionary algorithms, ant colony optimization, particle swarm optimization, bat algorithms, cuckoo search algorithms, and flower-pollination algorithms. Several dominance rules and a problem-specific local search were applied to the most promising resolution methods. Regarding the second problem, which also considers the buffer sizing, the developed algorithms were enhanced with a genetic discrete-event simulation model, whose primary function is to evaluate the value of the various objective functions. The demonstration of the associated resolution frameworks for both problems was validated through two industrial-cases
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Delattre, Maud. "Inférence statistique dans les modèles mixtes à dynamique Markovienne." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00765708.
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La première partie de cette thèse est consacrée à l'estimation par maximum de vraisemblance dans les modèles mixtes à dynamique markovienne. Nous considérons plus précisément des modèles de Markov cachés à effets mixtes et des modèles de diffusion à effets mixtes. Dans le Chapitre 2, nous combinons l'algorithme de Baum-Welch à l'algorithme SAEM pour estimer les paramètres de population dans les modèles de Markov cachés à effets mixtes. Nous proposons également des procédures spécifiques pour estimer les paramètres individuels et les séquences d' états cachées. Nous étudions les propriétés de cette nouvelle méthodologie sur des données simulées et l'appliquons sur des données réelles de nombres de crises d' épilepsie. Dans le Chapitre 3, nous proposons d'abord des modèles de diffusion à effets mixtes pour la pharmacocin étique de population. Nous en estimons les paramètres en combinant l'algorithme SAEM a un filtre de Kalman étendu. Nous étudions ensuite les propriétés asymptotiques de l'estimateur du maximum de vraisemblance dans des modèles de diffusion observés sans bruit de mesure continûment sur un intervalle de temps fixe lorsque le nombre de sujets tend vers l'infini. Le Chapitre 4 est consacré a la s élection de covariables dans des modèles mixtes généraux. Nous proposons une version du BIC adaptée au contexte de double asymptotique où le nombre de sujets et le nombre d'observations par sujet tendent vers l'infini. Nous présentons quelques simulations pour illustrer cette procédure.
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Mabon, Gwennaëlle. "Estimation non-paramétrique adaptative pour des modèles bruités." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB020/document.
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Dans cette thèse, nous nous intéressons au problème d'estimation de densité dans le modèle de convolution. Ce cadre correspond aux modèles avec erreurs de mesures additives, c'est-à-dire que nous observons une version bruitée de la variable d'intérêt. Pour mener notre étude, nous adoptons le point de vue de l'estimation non-paramétrique adaptative qui repose sur des procédures de sélection de modèle développées par Birgé & Massart ou sur les méthodes de Lepski. Cette thèse se divise en deux parties. La première développe des méthodes spécifiques d'estimation adaptative quand les variables d'intérêt et les erreurs sont des variables aléatoires positives. Ainsi nous proposons des estimateurs adaptatifs de la densité ou encore de la fonction de survie dans ce modèle, puis de fonctionnelles linéaires de la densité cible. Enfin nous suggérons une procédure d'agrégation linéaire. La deuxième partie traite de l'estimation adaptative de densité dans le modèle de convolution lorsque la loi des erreurs est inconnue. Dans ce cadre il est supposé qu'un échantillon préliminaire du bruit est disponible ou que les observations sont disponibles sous forme de données répétées. Les résultats obtenus pour des données répétées dans le modèle de convolution permettent d'élargir cette méthodologie au cadre des modèles linéaires mixtes. Enfin cette méthode est encore appliquée à l'estimation de la densité de somme de variables aléatoires observées avec du bruitIn this thesis, we are interested in nonparametric adaptive estimation problems of density in the convolution model. This framework matches additive measurement error models, which means we observe a noisy version of the random variable of interest. To carry out our study, we follow the paradigm of model selection developped by Birgé & Massart or criterion based on Lepski's method. The thesis is divided into two parts. In the first one, the main goal is to build adaptive estimators in the convolution model when both random variables of interest and errors are distributed on the nonnegative real line. Thus we propose adaptive estimators of the density along with the survival function, then of linear functionals of the target density. This part ends with a linear density aggregation procedure. The second part of the thesis deals with adaptive estimation of density in the convolution model when the distribution is unknown and distributed on the real line. To make this problem identifiable, we assume we have at hand either a preliminary sample of the noise or we observe repeated data. So, we can derive adaptive estimation with mild assumptions on the noise distribution. This methodology is then applied to linear mixed models and to the problem of density estimation of the sum of random variables when the latter are observed with an additive noise
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Baragatti, Meïli. "Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22100/document.
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Cette thèse se décompose en deux parties. Dans un premier temps nous nous intéressons à la sélection bayésienne de variables dans un modèle probit mixte.L'objectif est de développer une méthode pour sélectionner quelques variables pertinentes parmi plusieurs dizaines de milliers tout en prenant en compte le design d'une étude, et en particulier le fait que plusieurs jeux de données soient fusionnés. Le modèle de régression probit mixte utilisé fait partie d'un modèle bayésien hiérarchique plus large et le jeu de données est considéré comme un effet aléatoire. Cette méthode est une extension de la méthode de Lee et al. (2003). La première étape consiste à spécifier le modèle ainsi que les distributions a priori, avec notamment l'utilisation de l'a priori conventionnel de Zellner (g-prior) pour le vecteur des coefficients associé aux effets fixes (Zellner, 1986). Dans une seconde étape, nous utilisons un algorithme Metropolis-within-Gibbs couplé à la grouping (ou blocking) technique de Liu (1994) afin de surmonter certaines difficultés d'échantillonnage. Ce choix a des avantages théoriques et computationnels. La méthode développée est appliquée à des jeux de données microarray sur le cancer du sein. Cependant elle a une limite : la matrice de covariance utilisée dans le g-prior doit nécessairement être inversible. Or il y a deux cas pour lesquels cette matrice est singulière : lorsque le nombre de variables sélectionnées dépasse le nombre d'observations, ou lorsque des variables sont combinaisons linéaires d'autres variables. Nous proposons donc une modification de l'a priori de Zellner en y introduisant un paramètre de type ridge, ainsi qu'une manière de choisir les hyper-paramètres associés. L'a priori obtenu est un compromis entre le g-prior classique et l'a priori supposant l'indépendance des coefficients de régression, et se rapproche d'un a priori précédemment proposé par Gupta et Ibrahim (2007).Dans une seconde partie nous développons deux nouvelles méthodes MCMC basées sur des populations de chaînes. Dans le cas de modèles complexes ayant de nombreux paramètres, mais où la vraisemblance des données peut se calculer, l'algorithme Equi-Energy Sampler (EES) introduit par Kou et al. (2006) est apparemment plus efficace que l'algorithme classique du Parallel Tempering (PT) introduit par Geyer (1991). Cependant, il est difficile d'utilisation lorsqu'il est couplé avec un échantillonneur de Gibbs, et nécessite un stockage important de valeurs. Nous proposons un algorithme combinant le PT avec le principe d'échanges entre chaînes ayant des niveaux d'énergie similaires dans le même esprit que l'EES. Cette adaptation appelée Parallel Tempering with Equi-Energy Moves (PTEEM) conserve l'idée originale qui fait la force de l'algorithme EES tout en assurant de bonnes propriétés théoriques et une utilisation facile avec un échantillonneur de Gibbs.Enfin, dans certains cas complexes l'inférence peut être difficile car le calcul de la vraisemblance des données s'avère trop coûteux, voire impossible. De nombreuses méthodes sans vraisemblance ont été développées. Par analogie avec le Parallel Tempering, nous proposons une méthode appelée ABC-Parallel Tempering, basée sur la théorie des MCMC, utilisant une population de chaînes et permettant des échanges entre ellesThis thesis is divided into two main parts. In the first part, we propose a Bayesian variable selection method for probit mixed models. The objective is to select few relevant variables among tens of thousands while taking into account the design of a study, and in particular the fact that several datasets are merged together. The probit mixed model used is considered as part of a larger hierarchical Bayesian model, and the dataset is introduced as a random effect. The proposed method extends a work of Lee et al. (2003). The first step is to specify the model and prior distributions. In particular, we use the g-prior of Zellner (1986) for the fixed regression coefficients. In a second step, we use a Metropolis-within-Gibbs algorithm combined with the grouping (or blocking) technique of Liu (1994). This choice has both theoritical and practical advantages. The method developed is applied to merged microarray datasets of patients with breast cancer. However, this method has a limit: the covariance matrix involved in the g-prior should not be singular. But there are two standard cases in which it is singular: if the number of observations is lower than the number of variables, or if some variables are linear combinations of others. In such situations we propose to modify the g-prior by introducing a ridge parameter, and a simple way to choose the associated hyper-parameters. The prior obtained is a compromise between the conditional independent case of the coefficient regressors and the automatic scaling advantage offered by the g-prior, and can be linked to the work of Gupta and Ibrahim (2007).In the second part, we develop two new population-based MCMC methods. In cases of complex models with several parameters, but whose likelihood can be computed, the Equi-Energy Sampler (EES) of Kou et al. (2006) seems to be more efficient than the Parallel Tempering (PT) algorithm introduced by Geyer (1991). However it is difficult to use in combination with a Gibbs sampler, and it necessitates increased storage. We propose an algorithm combining the PT with the principle of exchange moves between chains with same levels of energy, in the spirit of the EES. This adaptation which we are calling Parallel Tempering with Equi-Energy Move (PTEEM) keeps the original idea of the EES method while ensuring good theoretical properties and a practical use in combination with a Gibbs sampler.Then, in some complex models whose likelihood is analytically or computationally intractable, the inference can be difficult. Several likelihood-free methods (or Approximate Bayesian Computational Methods) have been developed. We propose a new algorithm, the Likelihood Free-Parallel Tempering, based on the MCMC theory and on a population of chains, by using an analogy with the Parallel Tempering algorithm
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De, Faveri Joanne. "Spatial and temporal modelling for perennial crop variety selection trials." Thesis, 2013. http://hdl.handle.net/2440/83114.
Full textAbstract:
This thesis involves the investigation and development of methods for analysing data from variety selection trials in perennial crops. This involves identifying best varieties from data collected at multiple times in field trials, often from multiple locations and involving multiple traits. For accurate variety predictions the methods for analysis of such data need to account for the spatial correlation typically present in field trials and the temporal correlation induced by the repeated measures nature of the data. The methods also need to model the variety effects over time. The methods presented are based on the linear mixed model and estimation is performed using residual maximum likelihood (REML). Spatial analysis methods are applied to data from multiple harvest times for two perennial crop data sets. These analyses show that spatial correlation is evident and the spatial analysis methods improve model fit. Simulation studies also show the spatial analysis methods provide better predictions of variety effects (closer to the true effects). As the data from perennial crop variety selection trials is measured over time there is also a need to account for the temporal correlation between measurements. Separable models are presented that model the spatial and temporal residual covariance structure. These methods are suitable for large numbers of harvests. Application to a multi-harvest lucerne breeding data set shows these models to be an improvement on historical analysis approaches. At the genetic level the variety effects need to be modelled over time. Two approaches are presented. The first approach involves applying factor analytic models to variety by harvest effects and using clustering to aid in interpretation and selection. The second approach uses cubic smoothing spline random regression. These approaches are applied to data from two traits from a lucerne breeding trial and are shown to successfully model the variety by harvest effects and aid in selection. As data is usually obtained from multiple trials at different locations, the above approaches are extended to the multi-environment situation and applied to a multi-harvest, multi-environment lucerne data set. While the separable spatio-temporal residual models show an improvement on analysing each harvest time separately, they are very restrictive in that they assume common spatial correlation parameters across harvests (or traits). The initial spatial analyses on the two multi-harvest perennial crop data sets reveal that spatial correlation often varies between harvests and between traits. A more suitable non-separable covariance model is investigated that allows for differing spatial correlation across time or traits. The approach is based on the Multivariate Autoregressive model, initially for spatial correlation in one direction. Subsequently the model is extended to the two directional row-column situation using the theory of Multivariate Conditional Autoregressive models. These models are applied to the lucerne multi-harvest and multi-trait data using code written in R, and are shown in most cases to be a significant improvement to the separable residual models previously investigated.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2013
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Säfken, Benjamin. "Model choice and variable selection in mixed & semiparametric models." Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-5FA9-B.
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Li, Li. "Model Selection via Minimum Description Length." Thesis, 2011. http://hdl.handle.net/1807/31834.
Full textAbstract:
The minimum description length (MDL) principle originated from data compression literature and has been considered for deriving statistical model selection procedures. Most existing methods utilizing the MDL principle focus on models consisting of independent data, particularly in the context of linear regression. The data considered in this thesis are in the form of repeated measurements, and the exploration of MDL principle begins with classical linear mixed-effects models. We distinct two kinds of research focuses: one concerns the population parameters and the other concerns the cluster/subject parameters. When the research interest is on the population level, we propose a class of MDL procedures which incorporate the dependence structure within individual or cluster with data-adaptive penalties and enjoy the advantages of Bayesian information criteria. When the number of covariates is large, the penalty term is adjusted by data-adaptive structure to diminish the under selection issue in BIC and try to mimic the behaviour of AIC. Theoretical justifications are provided from both data compression and statistical perspectives. Extensions to categorical response modelled by generalized estimating equations and functional data modelled by functional principle components are illustrated. When the interest is on the cluster level, we use group LASSO to set up a class of candidate models. Then we derive a MDL criterion for this LASSO technique in a group manner to selection the final model via the tuning parameters. Extensive numerical experiments are conducted to demonstrate the usefulness of the proposed MDL procedures on both population level and cluster level.
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Feng, Shujuan. "Mixed-effect modeling of codon usage." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-12-2234.
Full textAbstract:
Logistic mixed effects models are used to determine whether optimal codons associate with two specific
properties of the expressed protein: solvent accessibility, aggregation propensity, or evolutionary conservation. Both random components and fixed structures in the models are decided by following certain selection procedures. More models are also developed by considering different factor combinations using the same selection procedure. The results show that evolutionary conservation is the most important factor for predicting for the optimal codon usage for most amino acids; aggregation propensity is also an important factor, and solvent accessibility is the least important factor for most amino acids.The results of this analysis are consistent with the previous literature, provide more
straightforward way to study the research question and also more information for the insight
relationships.text
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Lu, Darlene. "Clustering of temporal gene expression data with mixtures of mixed effects models." Thesis, 2019. https://hdl.handle.net/2144/34905.
Full textAbstract:
While time-dependent processes are important to biological functions, methods to leverage temporal information from large data have remained computationally challenging. In temporal gene-expression data, clustering can be used to identify genes with shared function in complex processes. Algorithms like K-Means and standard Gaussian mixture-models (GMM) fail to account for variability in replicated data or repeated measures over time and require a priori cluster number assumptions, evaluating many cluster numbers to select an optimal result. An improved penalized-GMM offers a computationally-efficient algorithm to simultaneously optimize cluster number and labels.
The work presented in this dissertation was motivated by mice bone-fracture models interested in determining patterns of temporal gene-expression during bone-healing progression. To solve this, an extension to the penalized-GMM was proposed to account for correlation between replicated data and repeated measures over time by introducing random-effects using a mixture of mixed-effects polynomial regression models and an entropy-penalized EM-Algorithm (EPEM).
First, performance of EPEM for different mixed-effects models were assessed with simulation studies and applied to the fracture-healing study. Second, modifications to address the high computational cost of EPEM were considered that either clustered subsets of data determined by predicted polynomial-order (S-EPEM) or used modified-initialization to decrease the initial burden (I-EPEM). Each was compared to EPEM and applied to the fracture-healing study. Lastly, as varied rates of fracture-healing were observed for mice with different genetic-backgrounds (strains), a new analysis strategy was proposed to compare patterns of temporal gene-expression between different mice-strains and assessed with simulation studies. Expression-profiles for each strain were treated as separate objects to cluster in order to determine genes clustered into different groups across strain.
We found that the addition of random-effects decreased accuracy of predicted cluster labels compared to K-Means, GMM, and fixed-effects EPEM. Polynomial-order optimization with BIC performed with highest accuracy, and optimization on subspaces obtained with singular-value-decomposition performed well. Computation time for S-EPEM was much reduced with a slight decrease in accuracy. I-EPEM was comparable to EPEM with similar accuracy and decrease in computation time. Application of the new analysis strategy on fracture-healing data identified several distinct temporal gene-expression patterns for the different strains.2021-02-27T00:00:00Z
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Ohinata, Ren. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model." Doctoral thesis, 2012. http://hdl.handle.net/11858/00-1735-0000-000D-F0A2-0.
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Saab, Rabih. "Nonparametric estimation of the mixing distribution in mixed models with random intercepts and slopes." Thesis, 2013. http://hdl.handle.net/1828/4548.
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Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form
l(G)=Σi log∫f(yi,γ) dG(γ)
where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω.
The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data.
Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach.Graduate
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rabihsaab@gmail.com
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"Addressing the Variable Selection Bias and Local Optimum Limitations of Longitudinal Recursive Partitioning with Time-Efficient Approximations." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.54792.
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abstract: Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.Dissertation/Thesis
Doctoral Dissertation Psychology 2019
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Cann, Benjamin. "Choosing a data frequency to forecast the quarterly yen-dollar exchange rate." Thesis, 2016. http://hdl.handle.net/1828/7587.
Full textAbstract:
Potentially valuable information about the underlying data generating process of a dependent variable is often lost when an independent variable is transformed to fit into the same sampling frequency as a dependent variable. With the mixed data sampling (MIDAS) technique and increasingly available data at high frequencies, the issue of choosing an optimal sampling frequency becomes apparent. We use financial data and the MIDAS technique to estimate thousands of regressions and forecasts in the quarterly, monthly, weekly, and daily sampling frequencies. Model fit and forecast performance measurements are calculated from each estimation and used to generate summary statistics for each sampling frequency so that comparisons can be made between frequencies. Our regression models contain an autoregressive component and five additional independent variables and are estimated with varying lag length specifications that incrementally increase up to five years of lags. Each regression is used to forecast a rolling, one and two-step ahead, static forecast of the quarterly Yen and U.S Dollar spot exchange rate. Our results suggest that it may be favourable to include high frequency variables for closer modeling of the underlying data generating process but not necessarily for increased forecasting performance.Graduate
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benjamincann@gmail.com
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Lawonn, Matthew James. "Breeding ecology and nest site selection of Kittlitz's murrelets on Kodiak Island, Alaska." Thesis, 2012. http://hdl.handle.net/1957/36245.
Full textAbstract:
The Kittlitz's murrelet (Brachyramphus brevirostris) is a rare member of the seabird family Alcidae that breeds in coastal areas of Alaska and Beringian Russia. The species belongs to the genus Brachyramphus, an unusual seabird taxon in which all three extant species nest non-colonially, situating their nests up to 75 km inland from coastal marine waters. This nesting strategy is different from that of most seabird species, which tend to nest colonially on remote islands or sea cliffs, where terrestrial predators are generally absent or cannot easily access nests. Within the genus Brachyramphus, Kittlitz's murrelet is notable because a majority of the global population appears to nest on the surface of the ground in rocky alpine habitat near inland or tidewater glaciers, foraging in adjacent marine waters influenced by glacial outflows. The unusual nesting habits of Kittlitz's murrelet have made the study of its nesting ecology difficult, and gaps therefore exist in our understanding of the species' breeding biology. Kittlitz's murrelet populations have declined substantially in core areas of its range, causing the U. S. Fish
and Wildlife Service to designate the species as a candidate for protection under the Endangered Species Act. A better understanding of Kittlitz's murrelet nesting ecology is crucial for determining potential causes of these declines and for future management of the species. To this end, I studied Kittlitz's murrelet breeding ecology and nest site selection during 2008-2011 on Kodiak Island, Alaska, in an unglaciated area that was recently found to have large numbers of accessible nests.
I and my colleagues found 53 active Kittlitz's murrelet nests in inland scree-dominated habitats and placed remote, motion-sensing cameras at 33 nests. Adults exchanged incubation duties at the nest every 24 or 48 h, almost exclusively during early morning twilight. Following hatching of eggs, parents provisioned their single nestling with an average of 3.9 to 4.8 fish per day, depending on the year. Parental visits to the nest during chick-rearing occurred primarily after sunrise in the early to mid-morning hours, and during evening twilight. Fish were delivered singly to the chick, and Pacific sand lance (Ammodytes hexapterus), a high-lipid forage fish, accounted for about 92% of all identifiable chick meal deliveries. Chick growth rates were high relative to confamilial species, consistent with the high quality of chick diets; the logistic growth rate constant (K) was 0.291, greater than that for any other semi-precocial alcid. Chicks fledged an average of 24.8 d after hatching and asymptotic chick body mass averaged about 135.5 g, approximately 58% of adult body mass. Age at fledging, asymptotic chick body mass (% adult mass), and the number of meal deliveries required to fledge a chick were all lower than or as low as any other species of semi-precocial alcid.
The average estimated nest survival rate during 2008-2011 was 0.093 (95% CI = 0.01–0.30), which is extremely low compared to other species in the family Alcidae, and is almost certainly insufficient to sustain a stable population. The primary causes of nest failure were depredation (47% of total nest fates), mostly by red foxes (Vulpes vulpes), and unexplained nestling mortality on the nest (21% of nest fates). Saxitoxin and/or pathogenic endoparasite burdens were observed in five of six necropsied chick carcasses, suggesting possible causes for chick mortality not directly attributable to predation.
Habitat characteristics of Kittlitz's murrelet nest sites differed significantly from unused sites at several scales. At a small scale (within 5 m of the nest), nest sites had a lower percent coverage of vegetation and higher percent coverage of intermediate-sized rocks (5–30 cm diameter), compared to randomly selected unused sites. Nest sites were also located on steeper, more north-facing slopes compared to randomly selected sites. Nest sites also had a lower percent coverage of vegetation than randomly-selected sites at larger scales (within 25 m and 50 m of the nest site). Nest sites were located significantly farther from the edge of densely-vegetated habitats than random sites. There was no evidence that nest sites were different from randomly-selected sites in terms of elevation, proximity to ridgelines, or proximity to the open ocean, although a low degree of variation within the study area for these habitat characteristics may have precluded detection of potential differences. Nest survival rates did not co-vary with slope, percent vegetation coverage, distance from vegetated edges, or percent cover of intermediate-sized rocks; however, this result may be an artifact of a limited sample size.
The results of this thesis will provide managers with a better understanding of the factors that may limit Kittlitz's murrelet nesting success, such as nest predation and forage fish availability, as well as factors that may influence the quality and distribution of Kittlitz’s murrelet nesting habitat in the future, given on-going and progressive climate change.Graduation date: 2013
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Maas, Bea. "Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yield." Doctoral thesis, 2013. http://hdl.handle.net/11858/00-1735-0000-0022-5E77-5.
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