HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by López-Romero, P. Articles by Carabaño, M. J. Search for Related Content PubMed PubMed Citation Articles by López-Romero, P. Articles by Carabaño, M. J.

Assessment of Homogeneity vs. Heterogeneity of Residual Variance in Random Regression Test-Day Models in a Bayesian Analysis

P. López-Romero^*, R. Rekaya and M. J. Carabaño^*

^* Departamento de Mejora Genética Animal. INIA. Carretera de A Coruña km 7. 28040 Madrid, Spain
Department of Animal and Dairy Science University of Georgia, Athens 30602

Corresponding author: P. Lopez-Romero; e-mail: plromero{at}cnb.uam.es.

Test-day first-lactation milk yields from Holstein cows were analyzed with a set of random regression models based on Legendre polynomials of varying order on additive genetic and permanent environmental effects. Homogeneity and heterogeneity of residual variance, assuming three and 30 arbitrary measurement error classes of different length were considered. Unknown parameters were estimated within a Bayesian framework. Bayes factors and a checking function for the cross-validation predictive densities of the data were the tools chosen for selecting among competing models. Residual variances obtained from 30 arbitrary intervals were nearly constant between d 70 and 300 and tended to increase towards the extremes of the lactation, especially at the onset. In early lactation, the temporary measurement errors were found to be larger and highly variable. A high order of the regression submodels employed for modeling the permanent environmental deviations tended to strongly correct the heterogeneity of the residual variance. Accordingly, the assumption of homogeneity of residual variance was the most plausible specification under both comparison criteria when the number of random regression coefficients was set to five. Otherwise, the heterogeneity assumption, using three or 30 error classes, was better supported, depending on the criterion and on the order of the submodel fitted for the permanent environmental effect.

Key Words: random regression test-day models • heterogeneity of residual variance • Bayesian analysis

Abbreviation key: AG = additive genetic, BF = Bayes factor, DGV = daily additive genetic variance, DPV = daily permanent environmental variance, ESS = effective sample size, IRV = intervals of residual variance, IW = inverse Wishart distribution, LMD = log marginal density of the data, MCV = Monte Carlo variance, MDD = marginal density of the data, MVN = multivariate normal distribution, PE = permanent environmental, RPT = repeatability model, RRC = random regression coefficients, RRM = random regression models, RV = residual variance, TD = test day

This article has been cited by other articles:

				J. Bohmanova, F. Miglior, and J. Jamrozik Use of test-day records beyond three hundred five days for estimation of three hundred five-day breeding values for production traits and somatic cell score of Canadian Holsteins J Dairy Sci, October 1, 2009; 92(10): 5314 - 5325. [Abstract] [Full Text] [PDF]

				C. Diaz, N. Moreno-Sanchez, J. Rueda, A. Reverter, Y. H. Wang, and M. J. Carabano Model selection in a global analysis of a microarray experiment J Anim Sci, January 1, 2009; 87(1): 88 - 98. [Abstract] [Full Text] [PDF]

				M. J. Carabano, C. Diaz, C. Ugarte, and M. Serrano Exploring the Use of Random Regression Models with Legendre Polynomials to Analyze Measures of Volume of Ejaculate in Holstein Bulls J Dairy Sci, February 1, 2007; 90(2): 1044 - 1057. [Abstract] [Full Text] [PDF]

				T. Strabel and J. Jamrozik Genetic analysis of milk production traits of polish black and white cattle using large-scale random regression test-day models. J Dairy Sci, August 1, 2006; 89(8): 3152 - 3163. [Abstract] [Full Text] [PDF]

				T. Strabel, J. Szyda, E. Ptak, and J. Jamrozik Comparison of Random Regression Test-Day Models for Polish Black and White Cattle J Dairy Sci, October 1, 2005; 88(10): 3688 - 3699. [Abstract] [Full Text] [PDF]

				M. J. Carabano, A. Moreno, P. Lopez-Romero, and C. Diaz Comparing alternative definitions of the contemporary group effect in Avilena Negra Iberica beef cattle using classical and Bayesian criteria J Anim Sci, December 1, 2004; 82(12): 3447 - 3457. [Abstract] [Full Text] [PDF]