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


Type:
Article
Title:
Bayesian regularized quantile regression: a robust alternative for genome-based prediction of skewed data
Creator:
Perez-Rodriguez, P.;
Perez-Rodriguez, P.
ORCID iD iconhttps://orcid.org/0000-0002-3202-1784
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Montesinos-Lopez, O.A.;
Montesinos-Lopez, O.A.
ORCID iD iconhttps://orcid.org/0000-0002-3973-6547
ScopusScopus ID
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Montesinos-Lopez, A.;
Crossa, J.
ORCID iD icon
Crossa, J.
ORCID iD iconhttps://orcid.org/0000-0001-9429-5855
ScopusScopus ID
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Year:
2020
Copyright:
CIMMYT 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
Journal volume:
8
Journal issue:
5
Pages:
713-722
DOI:
10.1016/j.cj.2020.04.009
Place of Publication:
Netherlands
Publisher:
Elsevier
Citation:
Bayesian regularized quantile regression: a robust alternative for genome-based prediction of skewed data. 2020. 8 (5) DOI: 10.1016/j.cj.2020.04.009 Elsevier.

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