Дисертації з теми "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.
Повний текст джерелаAlabiso, Audry. "Linear Mixed Model Selection by Partial Correlation." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587142724497829.
Повний текст джерелаAbraham, 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.
Повний текст джерелаTitle 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.
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.
Повний текст джерелаLan, Lan. "Variable Selection in Linear Mixed Model for Longitudinal Data." NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-05172006-211924/.
Повний текст джерелаWenren, 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.
Повний текст джерелаAtutey, 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.
Повний текст джерелаPan, 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.
Повний текст джерелаXiong, 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.
Повний текст джерелаGe, 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.
Повний текст джерелаRibbing, 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.
Повний текст джерелаStone, 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.
Повний текст джерелаPh.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
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.
Повний текст джерелаPotter, 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.
Повний текст джерелаLee, 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.
Повний текст джерелаQiu, 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.
Повний текст джерелаDepartment 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.
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.
Повний текст джерелаTitle 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.
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.
Повний текст джерелаSeries: Research Report Series / Department of Statistics and Mathematics
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.
Повний текст джерелаNuthmann, 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.
Повний текст джерелаAtwood, Chad Judson. "Effects of Alternative Silvicultural Treatments on Regeneration in the Southern Appalachians." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32997.
Повний текст джерелаThese 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
Cole, James Jacob. "Assessing Nonlinear Relationships through Rich Stimulus Sampling in Repeated-Measures Designs." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1587.
Повний текст джерелаMargevicius, 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.
Повний текст джерелаGuan, 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.
Повний текст джерелаPh. D.
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/.
Повний текст джерелаThe 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.
Huo, Shuning. "Bayesian Modeling of Complex High-Dimensional Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.
Повний текст джерелаDoctor 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.
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.
Повний текст джерелаConselho Nacional de Desenvolvimento Científico e Tecnológico
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.
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.
Повний текст джерелаIn 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
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.
Повний текст джерелаSchneider, 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.
Повний текст джерелаBuatois, Simon. "Novel pharmacometric methods to improve clinical drug development in progressive diseases." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC133.
Повний текст джерелаIn 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
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.
Повний текст джерелаThis 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
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.
Повний текст джерелаThe 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
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.
Повний текст джерелаMabon, Gwennaëlle. "Estimation non-paramétrique adaptative pour des modèles bruités." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB020/document.
Повний текст джерелаIn 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
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.
Повний текст джерелаThis 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
De, Faveri Joanne. "Spatial and temporal modelling for perennial crop variety selection trials." Thesis, 2013. http://hdl.handle.net/2440/83114.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2013
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.
Повний текст джерелаLi, Li. "Model Selection via Minimum Description Length." Thesis, 2011. http://hdl.handle.net/1807/31834.
Повний текст джерелаFeng, Shujuan. "Mixed-effect modeling of codon usage." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-12-2234.
Повний текст джерелаtext
Lu, Darlene. "Clustering of temporal gene expression data with mixtures of mixed effects models." Thesis, 2019. https://hdl.handle.net/2144/34905.
Повний текст джерела2021-02-27T00:00:00Z
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.
Повний текст джерелаSaab, Rabih. "Nonparametric estimation of the mixing distribution in mixed models with random intercepts and slopes." Thesis, 2013. http://hdl.handle.net/1828/4548.
Повний текст джерелаGraduate
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rabihsaab@gmail.com
"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.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Psychology 2019
Cann, Benjamin. "Choosing a data frequency to forecast the quarterly yen-dollar exchange rate." Thesis, 2016. http://hdl.handle.net/1828/7587.
Повний текст джерелаGraduate
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0508
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benjamincann@gmail.com
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.
Повний текст джерелаGraduation date: 2013
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.
Повний текст джерела