Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10305
Title: Modelling udder infection data using copula models for quadruples
Authors: MASSONNET, Goele 
JANSSEN, Paul 
DUCHATEAU, Luc 
Issue Date: 2009
Publisher: ELSEVIER SCIENCE BV
Source: JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 139(11). p. 3865-3877
Abstract: We study copula models for correlated infection times in the four udder quarters of dairy cows. Both a semi-parametric and a nonparametric approach are considered to estimate the marginal survival functions, taking into account the effect of a binary udder quarter level covariate. We use a two-stage estimation approach and we briefly discuss the asymptotic behaviour of the estimators obtained in the first and the second stage of the estimation. A pseudo-likelihood ratio test is used to select an appropriate copula from the power variance copula family that describes the association between the outcomes in a cluster. We propose a new bootstrap algorithm to obtain the p-value for this test. This bootstrap algorithm also provides estimates for the standard errors of the estimated parameters in the copula. The proposed methods are applied to the udder infection data. A small simulation study for a setting similar to the setting of the udder infection data gives evidence that the proposed method provides a valid approach to select an appropriate copula within the power variance copula family. (C) 2009 Elsevier B.V. All rights reserved.
Notes: [Massonnet, Goele; Janssen, Paul] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Duchateau, Luc] Univ Ghent, Dept Physiol & Biometr, B-9000 Ghent, Belgium.
Keywords: Bootstrap; Copula models; Pseudo-likelihood ratio test; Quadruples; Two-stage estimation
Document URI: http://hdl.handle.net/1942/10305
ISSN: 0378-3758
e-ISSN: 1873-1171
DOI: 10.1016/j.jspi.2009.05.025
ISI #: 000269458500017
Category: A1
Type: Journal Contribution
Validations: ecoom 2010
Appears in Collections:Research publications

Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.