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dc.contributor.author
Corrales Alvarez, J. D.  
dc.contributor.author
Munilla, S.  
dc.contributor.author
Cantet, Rodolfo Juan Carlos  
dc.date.available
2017-05-19T19:31:45Z  
dc.date.issued
2015-08  
dc.identifier.citation
Corrales Alvarez, J. D.; Munilla, S.; Cantet, Rodolfo Juan Carlos; Polynomial order selection in random regression models via penalizing adaptively the likelihood; Wiley; Journal Of Animal Breeding And Genetics-zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie; 132; 4; 8-2015; 281-288  
dc.identifier.issn
0931-2668  
dc.identifier.uri
http://hdl.handle.net/11336/16761  
dc.description.abstract
Orthogonal Legendre polynomials (LP) are used to model the shape of additive genetic and permanent environmental effects in random regression models (RRM). Frequently, the Akaike (AIC) and the Bayesian (BIC) information criteria are employed to select LP order. However, it has been theoretically shown that neither AIC nor BIC is simultaneously optimal in terms of consistency and efficiency. Thus, the goal was to introduce a method, ‘penalizing adaptively the likelihood’ (PAL), as a criterion to select LP order in RRM. Four simulated data sets and real data (60 513 records, 6675 Colombian Holstein cows) were employed. Nested models were fitted to the data, and AIC, BIC and PAL were calculated for all of them. Results showed that PAL and BIC identified with probability of one the true LP order for the additive genetic and permanent environmental effects, but AIC tended to favour over parameterized models. Conversely, when the true model was unknown, PAL selected the best model with higher probability than AIC. In the latter case, BIC never favoured the best model. To summarize, PAL selected a correct model order regardless of whether the ‘true’ model was within the set of candidates.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Wiley  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Legendre Polynomial  
dc.subject
Model Selection  
dc.subject
Penalizing Adaptively the Likelihood  
dc.subject
Random Regressions  
dc.subject.classification
Otras Producción Animal y Lechería  
dc.subject.classification
Producción Animal y Lechería  
dc.subject.classification
CIENCIAS AGRÍCOLAS  
dc.title
Polynomial order selection in random regression models via penalizing adaptively the likelihood  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2017-05-18T21:18:29Z  
dc.journal.volume
132  
dc.journal.number
4  
dc.journal.pagination
281-288  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Hoboken  
dc.description.fil
Fil: Corrales Alvarez, J. D.. Universidad de Antioquia; Colombia. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina  
dc.description.fil
Fil: Munilla, S.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina  
dc.description.fil
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.journal.title
Journal Of Animal Breeding And Genetics-zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/jbg.12130  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/jbg.12130/abstract