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Full-Text Articles in Other Statistics and Probability
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, …