Selection of the bandwidth parameter in a Bayesian kernel regression model for genomic-enabled prediction

DOI10.1007/s13253-015-0229-yzbMath1329.62449OpenAlexW2270508619WikidataQ58896000 ScholiaQ58896000MaRDI QIDQ906072

Paulino Pérez-Rodríguez, Jaime Cuevas, Jose Crossa, Sergio Perez-Elizalde

Publication date: 29 January 2016

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-015-0229-y

zbMATH Keywords

bandwidth selectionnonparametric regressiongenomic-enabled prediction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (1)

Statistical and computational challenges in whole genome prediction and genome-wide association analyses for plant and animal breeding

Uses Software

• R
• CODA
• rrBLUP
• BGLR

Cites Work

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• Sampling-Based Approaches to Calculating Marginal Densities
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