Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Identifieur interne : 000071 ( PubMed/Corpus ); précédent : 000070; suivant : 000072Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Auteurs : Osval A. Montesinos-L Pez ; Abelardo Montesinos-L Pez ; José Crossa ; Juan Burgue O ; Kent EskridgeSource :
- G3 (Bethesda, Md.) [ 2160-1836 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- genetics : Plant Diseases, Triticum, Zea mays.
- methods : Genomics.
- Algorithms, Bayes Theorem, Computer Simulation, Datasets as Topic, Logistic Models, Models, Genetic.
Abstract
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 BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.
DOI: 10.1534/g3.115.021154
PubMed: 26290569
Links to Exploration step
pubmed:26290569Le document en format XML
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<author><name sortKey="Crossa, Jose" sort="Crossa, Jose" uniqKey="Crossa J" first="José" last="Crossa">José Crossa</name>
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<term>Logistic Models</term>
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<front><div type="abstract" xml:lang="en">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 BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.</div>
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<Abstract><AbstractText>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 BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.</AbstractText>
<CopyrightInformation>Copyright © 2015 Montesinos-López et al.</CopyrightInformation>
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