Relevant bibliographies by topics / Mixed model varietal selection

Academic literature on the topic 'Mixed model varietal selection'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Mixed model varietal selection"

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Gonçalves, Elsa, and Antero Martins. "Efficient Assessment and Large-Scale Conservation of Intra-Varietal Diversity of Ancient Grapevine Varieties: Case Study Portugal." Plants 11, no. 15 (July 24, 2022): 1917. http://dx.doi.org/10.3390/plants11151917.

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There are thousands of ancient grapevine varieties in Europe, each one having a high level of intra-varietal diversity with regard to important economic traits (yield, soluble solids content, acidity, anthocyanins, and others). However, this potential has become exposed to a process of genetic erosion since the middle of the last century. The main objective of this work is to present experimental strategies for conservation and utilization of intra-varietal diversity. A concrete example is given about the actions performed in Portugal since 1978. Two main approaches for the conservation of intra-varietal diversity were performed: (1) strict conservation (in pots and in the field without experimental design) for future generations; and (2) conservation and, simultaneously, evaluation of the intra-varietal variability for selection to fulfil the immediate needs of the grape and wine sector (in the field with experimental design). More than 30,000 accessions of Portuguese autochthonous varieties are conserved. Using the theory of mixed models, intra-varietal diversity of the yield was found for the 59 varieties studied. The conservation and the evaluation of the intra-varietal diversity for quantitative traits will allow to extract high economic value, as well as to ensure its utilization to meet the objectives of the vine and wine sector.
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VIRK, D. S., D. B. PANDIT, M. A. SUFIAN, F. AHMED, M. A. B. SIDDIQUE, M. A. SAMAD, M. M. RAHMAN, et al. "REML IS AN EFFECTIVE ANALYSIS FOR MIXED MODELLING OF UNBALANCED ON-FARM VARIETAL TRIALS." Experimental Agriculture 45, no. 1 (January 2009): 77–91. http://dx.doi.org/10.1017/s0014479708007047.

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SUMMARYOn-farm participatory varietal selection (PVS) trials are often of two types: mother trials (with all of the entries) and baby trials (each having one, or very few of the entries from the mother trials). We conducted PVS trials on 17 wheat varieties in 12 villages of four districts of Bangladesh over three years but the data were highly unbalanced. Both quantitative and qualitative traits were measured in the on-farm trials. The factors in the trials were both fixed effects (varieties and districts) and random (years and farmers). We used the residual or restricted maximum likelihood (REML) analysis for the mixed model for quantitative traits. For qualitative data on farmers' perceptions, logistic regression procedures were used that are equally applicable to balanced and unbalanced data sets. The REML analysis provided adjusted mean values for quantitative traits for all the varieties, for the mother and baby trials separately, using the data from all years and all locations. It identified varieties BAW 1006 and BAW 1008 that yielded 19–30% more than the control Kanchan and also had a higher 1000-grain weight, were at least as early to flower and had a high overall ranking by farmers in the mother trials. The logistic regression analysis of perception data agreed with the results of the REML analysis as these varieties were most preferred by farmers for grain yield, earlier maturity and better chapatti making quality. The less labour-intensive method of recording qualitative perceptions can usefully replace actual yield measurements, particularly when validated by other participatory measures such as intended and actual adoption. In 2005, BAW 1006 was released as BARI Gom 23 or Bijoy and BAW 1008 as BARI Gom 24 or Prodip for the whole of Bangladesh. The validity of the results of the REML analysis was confirmed by the high early adoption trends of the identified varieties. Since REML is an effective analysis for unbalanced PVS trial data using a mixed model, its wider use by researchers would increase the value of the PVS process.
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Mann, Gulay, Helen Allen, Matthew K. Morell, Zena Nath, Peter Martin, John Oliver, Brian Cullis, and Alison Smith. "Comparison of small-scale and large-scale extensibility of dough produced from wheat flour." Australian Journal of Agricultural Research 56, no. 12 (2005): 1387. http://dx.doi.org/10.1071/ar05132.

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Extensibility and dough strength are key traits for varietal selection in most wheat-breeding programs. As there are several techniques for measuring these traits there is interest in examining the agreement between methods in terms of genotypic (varietal) rankings. We investigated this issue using 2 different extension methods; namely, small-scale (modified Kieffer) and large-scale (Brabender Extensograph) methods. Data were obtained from a doubled-haploid population (190 lines) from a Chara (high extensibility, excellent dough strength) × WW2449 (low extensibility, poor dough strength) cross that was grown in a field trial at the Wagga Wagga Agricultural Institute (WWAI) in 2000. Six extensional rheological traits were measured and compared according to a multivariate mixed statistical model. The estimated genetic correlation matrix for 4 of the 6 extensibility traits (R_Max, area, height, and resistance at 5 cm extension) revealed that for these dough strength related parameters, both methods were measuring equivalent traits. Comparisons of the extensibility traits length and extensibility at Rmax demonstrated that, although substantial amounts of the variance are controlled by the same glutenin loci, the traits differed in the allocation of variance across the loci, and the sources and magnitude of non-genetic variance. The data verified that small-scale testing is a robust and efficient alternative to large-scale testing for both commercial breeding and research.
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de Carvalho, Sérgio Alves, William Mário de Carvalho Nunes, José Belasque, Marcos Antonio Machado, José Croce-Filho, Clive H. Bock, and Zaid Abdo. "Comparison of Resistance to Asiatic Citrus Canker Among Different Genotypes of Citrus in a Long-Term Canker-Resistance Field Screening Experiment in Brazil." Plant Disease 99, no. 2 (February 2015): 207–18. http://dx.doi.org/10.1094/pdis-04-14-0384-re.

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Asiatic citrus canker (ACC) is caused by Xanthomonas citri subsp. citri. The disease results in yield loss and renders fruit unfit for the fresh market. A 6-year study in Paraná State, Brazil, was conducted to compare the susceptibility of 186 genotypes of citrus representing sweet orange (Citrus sinensis), mandarin (C. reticulata), Mediterranean mandarin (C. deliciosa), Clementine mandarin (C. clementina), Satsuma mandarin (C. unshiu), sour orange (C. aurantium), lemon (C. limon), sweet lime (C. aurantifolia), grapefruit (C. paradisi), and four hybrids (C. reticulata × Citrus sp., C. reticulata × C. paradisi, C. reticulata × C. sinensis, and C. unshiu × C. sinensis). Sweet orange (C. sinensis) was represented by the most genotypes (n = 141). The number of lesions per leaf was assessed 18 times from 2005 to 2010 (up to 4 times per year). The data were analyzed using mixed-model analysis of fixed and random effects, which showed a total of six resistance-susceptibility groupings of species and hybrids. Based on species, the most resistant genotypes, on average, included Satsuma and lemon (mean lesions per leaf = 4.32 and 4.26, respectively), and the most susceptible genotypes were grapefruit and sweet lime, with 14.84 and 10.96 lesions per leaf, respectively. Genotypes of mandarin, sour orange, Mediterranean mandarin, and sweet orange had intermediate severity (5.48 to 9.56 lesions per leaf). The hybrids also showed a range of ACC severity but all were in the more resistant groupings (5.26 to 7.35 lesions per leaf). No genotype was immune to ACC. The most resistant genotype was ‘Muscia’ (C. reticulata) and the most susceptible was ‘Valencia Frost’ (C. sinensis) (1.86 and 14.78 lesions per leaf, respectively). Approximately one-sixth of the genotypes showed a negative relationship of mean lesions per leaf with time, suggesting increasing resistance as they aged, due to a reduction in either new flush or plant size and structure. These results of the relative susceptibility of different citrus genotypes can be used in future research and to assist in varietal selection or for breeding purposes both within Brazil and other regions where ACC is an issue.
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Acheampong, Patricia Pinamang, Victor Owusu, and Gyeile Nurah. "How does Farmer Preference matter in Crop variety Adoption? The case of Improved Cassava varieties’ Adoption in Ghana." Open Agriculture 3, no. 1 (November 1, 2018): 466–77. http://dx.doi.org/10.1515/opag-2018-0052.

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Abstract Ghana’s National Agricultural Research Systems have officially released 24 improved cassava varieties, which are high yielding, disease and pest resistant and early maturing. However, adoption of these varieties by mainly smallholder farmers is very low, leading to low yields and incomes. The purpose of this study was to contribute to the development and adoption of improved cassava varieties by assessing the preferences of farmers for cassava variety traits. The study explored Ghanaian cassava producers’ decision-making behaviour towards variety selection and the values they place on different cassava traits. It employed mixed logit and latent class models to estimate the values place on cassava traits, by using choice experiment data of 450 cassava producers from Ghana. Results revealed farmers’ preferences for longevity of root storage in the soil and disease resistance traits of cassava. The latent class model revealed that male youths were more likely to participate in improved varieties that take into account in-soil storage and multiple usages. The need for agricultural research systems to focus on other traits in addition to high yielding and disease resistance in order to boost adoption and increase production is imperative.
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Jiang, Jiming, J. Sunil Rao, Zhonghua Gu, and Thuan Nguyen. "Fence methods for mixed model selection." Annals of Statistics 36, no. 4 (August 2008): 1669–92. http://dx.doi.org/10.1214/07-aos517.

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Müller, Samuel, J. L. Scealy, and A. H. Welsh. "Model Selection in Linear Mixed Models." Statistical Science 28, no. 2 (May 2013): 135–67. http://dx.doi.org/10.1214/12-sts410.

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Malouche, Dhafer. "Mixed Graphical Model Selection Using Holm's Procedure." Communications in Statistics - Theory and Methods 38, no. 9 (April 28, 2009): 1453–64. http://dx.doi.org/10.1080/03610920802455019.

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Peng, Heng, and Ying Lu. "Model selection in linear mixed effect models." Journal of Multivariate Analysis 109 (August 2012): 109–29. http://dx.doi.org/10.1016/j.jmva.2012.02.005.

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Buscemi, Simona, and Antonella Plaia. "Model selection in linear mixed-effect models." AStA Advances in Statistical Analysis 104, no. 4 (October 28, 2019): 529–75. http://dx.doi.org/10.1007/s10182-019-00359-z.

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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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Books on the topic "Mixed model varietal selection"

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Selection index and introduction to mixed model methods. Boca Raton: CRC Press, 1993.

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Gebers, Bart Dwayne. Wine grape vineyard computer simulation model for varietal selection. 1994.

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Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 2. Mixed-model and Bayesian Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0019.

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When the full pedigree of individuals whose values (records) were used in the selection decisions during an experiment (or breeding program) is known, LS analysis can be replaced by mixed models and their Bayesian extensions. In this setting, REML can be used to estimate genetic variances and BLUP can be used to estimate the mean breeding value in any given generation. The latter allows for genetic trends to be separated from environmental trends without the need for a control population. Under the infinitesimal model setting (wherein selection-induced allele-frequency changes are small during the course of the experiment), the use of the relationship matrix in a BLUP analysis accounts for drift, nonrandom mating, and linkage disequilibrium.
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Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
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Walsh, Bruce, and Michael Lynch. Associative Effects: Competition, Social Interactions, Group and Kin Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0022.

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The phenotypes of those individuals with which an focal individual interacts often influences the trait value in the focal individual. Maternal effects is a classic example of this phenomena, as is fitness. If these traits are heritable, then the selection response depends on both the change in the direct effects influencing a target trait and the associative effects contributed by interacting individuals. In such a setting, the breeder's equation no longer holds, as the problem is now a multiple trait one. This chapter examines the theory of response under models with both direct and associative effects, which can lead to a reversed response (a trait selected to increase instead decreases). The evolution of behavioral traits, including the evolution of altruism, is best handled using this approach. Further, kin and group selection follow as special cases of the gerenal model under multilevel selection. This chapter also examines how mixed models can be used estimate model parameters.
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Walsh, Bruce, and Michael Lynch. Short-term Changes in the Mean: 2. Truncation and Threshold Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0014.

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The selection intensity, the mean change in a trait within a generation expressed in phenotypic standard deviations, provides an important metric for comparing the strength of selection over designs. Further, under truncation selection (only individuals above some threshold leave offspring), the selection intensity is a function of the fraction saved, and hence the breeder's equation is often expressed in terms of the selection intensity. An important special case of truncation selection is a threshold trait, wherein an individual only expresses a particular phenotype when its underlying liability value exceeds some threshold. This chapter examines selection on such traits, and generalizes this binary-trait setting (with binomial residuals) to other classes of discrete traits, wherein some underling linear model (generating the threshold) is this transformed via a generalized linear mixed model into an observed trait value.
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Okasha, Samir. Risk, Rational Choice, and Evolution. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815082.003.0009.

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Decision-theoretic ideas arise in two areas of biology: risk-sensitive foraging, and the theory of evolution in variable environments. The former concerns the actual behavioural choices that organisms make, the latter the ‘choices’ made by natural selection. A natural suggestion is that both sorts of choices can be modelled in terms of expected utility maximization, the standard theory of rational decision in the face of risk. However, this is only true under particular model assumptions; it does not hold in situations involving a combination of aggregate and idiosyncratic risk. Mixed strategies further complicate the relation between rational and biologically optimal risk preferences. This implies a limit on the validity of the organism-as-rational-agent heuristic as a tool for understanding evolved behaviour.
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Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.
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Book chapters on the topic "Mixed model varietal selection"

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Kinney, Satkartar K., and David B. Dunson. "Bayesian Model Uncertainty in Mixed Effects Models." In Random Effect and Latent Variable Model Selection, 37–62. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_3.

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Cai, Bo, and David B. Dunson. "Bayesian Variable Selection in Generalized Linear Mixed Models." In Random Effect and Latent Variable Model Selection, 63–91. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_4.

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Fang, Yong, K. K. Lai, and Shou-Yang Wang. "A Fuzzy Mixed Projects and Securities Portfolio Selection Model." In Fuzzy Systems and Knowledge Discovery, 931–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_117.

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Crainiceanu, Ciprian M. "Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models." In Random Effect and Latent Variable Model Selection, 3–17. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_1.

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Bentler, Peter M., and Jiajuan Liang. "A Unified Approach to Two-Level Structural Equation Models and Linear Mixed Effects Models." In Random Effect and Latent Variable Model Selection, 95–119. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_5.

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Zhang, Daowen, and Xihong Lin. "Variance Component Testing in Generalized Linear Mixed Models for Longitudinal/Clustered Data and other Related Topics." In Random Effect and Latent Variable Model Selection, 19–36. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_2.

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Lu, Chen, Dong Liang, Dongxu Wang, and Yilin Zhao. "Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model." In Lecture Notes in Electrical Engineering, 2581–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_315.

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Erbay, Hasan, and Nihan Yıldırım. "Technology Selection for Digital Transformation: A Mixed Decision Making Model of AHP and QFD." In Proceedings of the International Symposium for Production Research 2018, 480–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92267-6_41.

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García Portilla, Jason. "Case Selection Criteria, Methods, and Data Treatment." In “Ye Shall Know Them by Their Fruits”, 259–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78498-0_17.

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AbstractThis chapter examines the criteria for selecting the cases analysed in this study. It explains the application of critical discourse analysis (CDA) as its principal empirical method. It discusses CDA methods and data treatment as well as the empirical analysis of CDA results. Finally, it compares and summarises the case selection criteria.Four countries were compared: Switzerland, Uruguay, Cuba, and Colombia. These cases are also linked to the correlated variables in the quantitative model and to the sufficient conditions in the QCA. Cases were selected based on the “extreme case method”.Each of the selected countries serves as a proxy of a larger group of countries (Latin American Strong Catholic, Secular, Communism, Protestant or mixed Old World.).Critical discourse analysis (CDA) and religion constitute the qualitative method (micro-component). Sixty semi-structured interviews were conducted and analysed using memos and open coding.
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "General Elements of Genomic Selection and Statistical Learning." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 1–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_1.

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AbstractNowadays, huge data quantities are collected and analyzed for delivering deep insights into biological processes and human behavior. This chapter assesses the use of big data for prediction and estimation through statistical machine learning and its applications in agriculture and genetics in general, and specifically, for genome-based prediction and selection. First, we point out the importance of data and how the use of data is reshaping our way of living. We also provide the key elements of genomic selection and its potential for plant improvement. In addition, we analyze elements of modeling with machine learning methods applied to genomic selection and stress their importance as a predictive methodology. Two cultures of model building are analyzed and discussed: prediction and inference; by understanding modeling building, researchers will be able to select the best model/method for each circumstance. Within this context, we explain the differences between nonparametric models (predictors are constructed according to information derived from data) and parametric models (all the predictors take predetermined forms with the response) as well their type of effects: fixed, random, and mixed. Basic elements of linear algebra are provided to facilitate understanding the contents of the book. This chapter also contains examples of the different types of data using supervised, unsupervised, and semi-supervised learning methods.
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Conference papers on the topic "Mixed model varietal selection"

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Wang, He, Xiaowei Zhu, S. Jack Hu, Zhongqin Lin, and Guanlong Chen. "Product Family Design to Minimize Manufacturing Complexity in Mixed-Model Assembly Systems." In ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84045.

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The number of product varieties offered by manufacturers has increased drastically as a result of the paradigm change from mass production to mass customization. High product variety introduces complexity in manufacturing. In this paper, we discuss product family design to minimize manufacturing complexity in manual, mixed-model assembly systems. A concept of relative complexity is proposed to help system designers making decisions on product family selection. Based on the relative complexity, a model for product variety selection is then proposed to find the best combination of product variants to be provided to the market in order to maximize market share and minimize manufacturing complexity. A numerical example is provided to demonstrate the approach.
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Zhu, Xiaowei, Hui Wang, S. Jack Hu, and Yoram Koren. "Build Sequence Scheduling to Minimize Complexity in Mixed-Model Assembly Lines." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59061.

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Build sequence scheduling is an important topic in mixed-model production. It is to determine the order of products being built in the assembly line. Significant research has been conducted to determine good sequences based on various criteria. For example, in Just-In-Time production systems, optimal sequences are searched to minimize the variation in the rate at which different parts were consumed. This paper discusses the selection of optimal build sequences based on complexity introduced by product variety in mixed-model assembly line. The complexity was defined as the information entropy that operator processes during assembly, which indirectly measures the human performance in making choices, such as selecting parts, tools, fixtures, and assembly procedures in a multi-product, multi-stage, manual assembly environment. In an earlier paper by the authors, a simple version of complexity measure has been developed for i.i.d. (independent identically distributed) sequences. This paper extends the concept and takes into account the sequential dependence of the choices and its impact on build sequence schedules. A model based on Hidden Markov Chain is proposed to model the sequence scheduling problem with the constraints by spacing rules. Methodologies developed in this paper enhance the previous work on modeling complexity, and provide solution strategies for build sequence scheduling to minimize complexity.
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He, Ming. "Research on Feature Selection Algorithm Based on Mixed Model." In 2008 International Conference on Computer and Electrical Engineering (ICCEE). IEEE, 2008. http://dx.doi.org/10.1109/iccee.2008.38.

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Xiaoxia Huang and Yaqin Zhou. "A mixed projects and securities portfolio selection and adjustment model." In 2014 11th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2014. http://dx.doi.org/10.1109/icsssm.2014.6874075.

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Behzadi, Seyed, and Vladimir Alvarado. "Selection of Three-Phase Relative Permeability Model for Mixed-Wet Reservoirs." In SPE Western Regional Meeting. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/132849-ms.

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Streckel, B., J. F. Evers-Senne, and R. Koch. "Lens model selection for a markerless AR tracking system." In Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05). IEEE, 2005. http://dx.doi.org/10.1109/ismar.2005.38.

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Yu, Dalei, Kelvin K. W. Yau, and Chang Ding. "Information Based Model Selection Criterion for Binary Response Generalized Linear Mixed Models." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.21.

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Wei, Linjing, Wenke Dong, Yangping Wang, and Shirun Gan. "Multi-trait Selection Model of Dairy Cow Based on the Mixed Leapfrog Algorithm." In 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2019. http://dx.doi.org/10.1109/dasc/picom/cbdcom/cyberscitech.2019.00033.

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Abbas, Hazem M. "Mixed-coded evolutionary algorithm for Gaussian mixture maximum likelihood clustering with model selection." In the 12th annual conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830483.1830631.

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Thoenburin, Phongsaphak, and Chawis Boonmee. "Mixed-Integer Linear Programming Model for Safe Zone Selection during Air Pollution Disaster." In 2022 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2022. http://dx.doi.org/10.1109/dasa54658.2022.9765238.

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Reports on the topic "Mixed model varietal selection"

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Ficht, Thomas, Gary Splitter, Menachem Banai, and Menachem Davidson. Characterization of B. Melinensis REV 1 Attenuated Mutants. United States Department of Agriculture, December 2000. http://dx.doi.org/10.32747/2000.7580667.bard.

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Brucella Mutagenesis (TAMU) The working hypothesis for this study was that survival of Brucella vaccines was directly related to their persistence in the host. This premise is based on previously published work detailing the survival of the currently employed vaccine strains S19 and Rev 1. The approach employed signature-tagged mutagenesis to construct mutants interrupted in individual genes, and the mouse model to identify mutants with attenuated virulence/survival. Intracellular survival in macrophages is the key to both reproductive disease in ruminants and reticuloendothelial disease observed in most other species. Therefore, the mouse model permitted selection of mutants of reduced intracellular survival that would limit their ability to cause reproductive disease in ruminants. Several classes of mutants were expected. Colonization/invasion requires gene products that enhance host-agent interaction or increase resistance to antibacterial activity in macrophages. The establishment of chronic infection requires gene products necessary for intracellular bacterial growth. Maintenance of chronic infection requires gene products that sustain a low-level metabolism during periods characterized little or no growth (1, 2). Of these mutants, the latter group was of greatest interest with regard to our originally stated premise. However, the results obtained do not necessarily support a simplistic model of vaccine efficacy, i.e., long-survival of vaccine strains provides better immunity. Our conclusion can only be that optimal vaccines will only be developed with a thorough understanding of host agent interaction, and will be preferable to the use of fortuitous isolates of unknown genetic background. Each mutant could be distinguished from among a group of mutants by PCR amplification of the signature tag (5). This approach permitted infection of mice with pools of different mutants (including the parental wild-type as a control) and identified 40 mutants with apparently defective survival characteristics that were tentatively assigned to three distinct classes or groups. Group I (n=13) contained organisms that exhibited reduced survival at two weeks post-infection. Organisms in this group were recovered at normal levels by eight weeks and were not studied further, since they may persist in the host. Group II (n=11) contained organisms that were reduced by 2 weeks post infection and remained at reduced levels at eight weeks post-infection. Group III (n=16) contained mutants that were normal at two weeks, but recovered at reduced levels at eight weeks. A subset of these mutants (n= 15) was confirmed to be attenuated in mixed infections (1:1) with the parental wild-type. One of these mutants was eliminated from consideration due to a reduced growth rate in vitro that may account for its apparent growth defect in the mouse model. Although the original plan involved construction of the mutant bank in B. melitensis Rev 1 the low transformability of this strain, prevented accumulation of the necessary number of mutants. In addition, the probability that Rev 1 already carries one genetic defect increases the likelihood that a second defect will severely compromise the survival of this organism. Once key genes have been identified, it is relatively easy to prepare the appropriate genetic constructs (knockouts) lacking these genes in B. melitensis Rev 1 or any other genetic background. The construction of "designer" vaccines is expected to improve immune protection resulting from minor sequence variation corresponding to geographically distinct isolates or to design vaccines for use in specific hosts. A.2 Mouse Model of Brucella Infection (UWISC) Interferon regulatory factor-1-deficient (IRF-1-/- mice have diverse immunodeficient phenotypes that are necessary for conferring proper immune protection to intracellular bacterial infection, such as a 90% reduction of CD8+ T cells, functionally impaired NK cells, as well as a deficiency in iNOS and IL-12p40 induction. Interestingly, IRF-1-/- mice infected with diverse Brucella abortus strains reacted differently in a death and survival manner depending on the dose of injection and the level of virulence. Notably, 50% of IRF-1-/- mice intraperitoneally infected with a sublethal dose in C57BL/6 mice, i.e., 5 x 105 CFU of virulent S2308 or the attenuated vaccine S19, died at 10 and 20 days post-infection, respectively. Interestingly, the same dose of RB51, an attenuated new vaccine strain, did not induce the death of IRF-1-/- mice for the 4 weeks of infection. IRF-1-/- mice infected with four more other genetically manipulated S2308 mutants at 5 x 105 CFU also reacted in a death or survival manner depending on the level of virulence. Splenic CFU from C57BL/6 mice infected with 5 x 105 CFU of S2308, S19, or RB51, as well as four different S2308 mutants supports the finding that reduced virulence correlates with survival Of IRF-1-/- mice. Therefore, these results suggest that IRF-1 regulation of multi-gene transcription plays a crucial role in controlling B. abortus infection, and IRF-1 mice could be used as an animal model to determine the degree of B. abortus virulence by examining death or survival. A3 Diagnostic Tests for Detection of B. melitensis Rev 1 (Kimron) In this project we developed an effective PCR tool that can distinguish between Rev1 field isolates and B. melitensis virulent field strains. This has allowed, for the first time, to monitor epidemiological outbreaks of Rev1 infection in vaccinated flocks and to clearly demonstrate horizontal transfer of the strain from vaccinated ewes to unvaccinated ones. Moreover, two human isolates were characterized as Rev1 isolates implying the risk of use of improperly controlled lots of the vaccine in the national campaign. Since atypical B. melitensis biotype 1 strains have been characterized in Israel, the PCR technique has unequivocally demonstrated that strain Rev1 has not diverted into a virulent mutant. In addition, we could demonstrate that very likely a new prototype biotype 1 strain has evolved in the Middle East compared to the classical strain 16M. All the Israeli field strains have been shown to differ from strain 16M in the PstI digestion profile of the omp2a gene sequence suggesting that the local strains were possibly developed as a separate branch of B. melitensis. Should this be confirmed these data suggest that the Rev1 vaccine may not be an optimal vaccine strain for the Israeli flocks as it shares the same omp2 PstI digestion profile as strain 16M.
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