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Full-Text Articles in Other Statistics and Probability
Genomic Bayesian Prediction Model For Count Data With Genotype X Environment Interaction, Abelardo Montesinos-López, Osval A. Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge, Esteban Falconi-Castillo, Xinyao He, Pawan Singh, Karen Cichy
Genomic Bayesian Prediction Model For Count Data With Genotype X Environment Interaction, Abelardo Montesinos-López, Osval A. Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge, Esteban Falconi-Castillo, Xinyao He, Pawan Singh, Karen Cichy
Department of Statistics: Faculty Publications
Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (nT ) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (nT ). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic …
Genomic-Enabled Prediction Of Ordinal Data With Bayesian Logistic Ordinal Regression, Osval A. Montesinos-López, Abelardo Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge
Genomic-Enabled Prediction Of Ordinal Data With Bayesian Logistic Ordinal Regression, Osval A. Montesinos-López, Abelardo Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge
Department of Statistics: Faculty Publications
Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPORmodel …
Threshold Models For Genome-Enabled Prediction Of Ordinal Categorical Traits In Plant Breeding, Osval A. Montesinos-López, Abelardo Montesinos-López, Paulino Pérez-Rodríguez, Gustavo De Los Campos, Kent M. Eskridge, José Crossa
Threshold Models For Genome-Enabled Prediction Of Ordinal Categorical Traits In Plant Breeding, Osval A. Montesinos-López, Abelardo Montesinos-López, Paulino Pérez-Rodríguez, Gustavo De Los Campos, Kent M. Eskridge, José Crossa
Department of Statistics: Faculty Publications
Categorical scores for disease susceptibility or resistance often are recorded in plant breeding. The aim of this study was to introduce genomic models for analyzing ordinal characters and to assess the predictive ability of genomic predictions for ordered categorical phenotypes using a threshold model counterpart of the Genomic Best Linear Unbiased Predictor (i.e., TGBLUP). The threshold model was used to relate a hypothetical underlying scale to the outward categorical response. We present an empirical application where a total of nine models, five without interaction and four with genomic x environment interaction (G·E) and genomic additive x additive x environment interaction …
Parametric And Nonparametric Statistical Methods For Genomic Selection Of Traits With Additive And Epistatic Genetic Architectures, Reka Howard, Alicia L. Carriquiry, William D. Beavis
Parametric And Nonparametric Statistical Methods For Genomic Selection Of Traits With Additive And Epistatic Genetic Architectures, Reka Howard, Alicia L. Carriquiry, William D. Beavis
Department of Statistics: Faculty Publications
Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, …