Bayesian regularized quantile regression: a robust alternative for genome-based prediction of skewed data

dc.creatorPerez-Rodriguez, P.
dc.creatorMontesinos-Lopez, O.A.
dc.creatorMontesinos-Lopez, A.
dc.creatorCrossa, J.
dc.date2020-11-28T01:10:16Z
dc.date2020-11-28T01:10:16Z
dc.date2020
dc.date.accessioned2023-07-17T20:06:24Z
dc.date.available2023-07-17T20:06:24Z
dc.identifierhttps://hdl.handle.net/10883/21023
dc.identifier10.1016/j.cj.2020.04.009
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512809
dc.descriptionGenomic prediction (GP) has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle. A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself. In this study, we propose to use Bayesian regularized quantile regression (BRQR) in the context of GP; the model has been successfully used in other research areas. We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression (BRR; equivalent to genomic best linear unbiased predictor, GBLUP). In addition, BLUP can be used with pedigree information obtained from the coefficient of coancestry (ABLUP). We have found that the prediction ability of BRQR is comparable to that of BRR and, in some cases, better; it also has the potential to efficiently deal with outliers. A program written in the R statistical package is available as Supplementary material.
dc.description713-722
dc.languageEnglish
dc.publisherElsevier
dc.relationhttps://www.sciencedirect.com/science/article/pii/S2214514120300787?via%3Dihub#s0130
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source5
dc.source8
dc.source2214-5141
dc.sourceThe Crop Journal
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectLaplace Distribution
dc.subjectRobust Regression
dc.subjectBayesian Quantile Regression
dc.subjectGenomic Enabled Prediction
dc.subjectGENOMICS
dc.subjectBAYESIAN THEORY
dc.subjectFUNCTIONAL ANALYSIS
dc.titleBayesian regularized quantile regression: a robust alternative for genome-based prediction of skewed data
dc.typeArticle
dc.typePublished Version
dc.coverageNetherlands


Este ítem pertenece a la siguiente institución