Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data
Type:
Title:
Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data
Author:
Perez-Rodriguez, P.;
Flores-Galarza, S.;
Vaquera-Huerta, H.;
Valle-Paniagua, D.H. del;
Montesinos-Lopez, O.A.;
https://orcid.org/0000-0002-3973-6547
Scopus ID
Items in this Repository
View
Crossa, J.
Flores-Galarza, S.;
Vaquera-Huerta, H.;
Valle-Paniagua, D.H. del;
Montesinos-Lopez, O.A.;
Montesinos-Lopez, O.A.


View
Crossa, J.

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:
The Plant Genome
Journal volume:
13
Journal issue:
2; art. e20021
DOI:
Place of Publication:
Madison, WI (USA)
Publisher:
Crop Science Society of America
Citation:
Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data. 2020. Perez-Rodriguez, P.; Flores-Galarza, S.; Vaquera-Huerta, H.; Valle-Paniagua, D.H. del; Montesinos-Lopez, O.A.; Crossa, J. 13 (2)(art. e20021) DOI: 10.1002/tpg2.20021 Crop Science Society of America.