Modelling udder infection data using copula models for quadruples

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.

[1]  Thomas A. Louis,et al.  Assessing gamma frailty models for clustered failure time data , 1995, Lifetime data analysis.

[2]  M. Sklar Fonctions de repartition a n dimensions et leurs marges , 1959 .

[3]  Danyu Lin,et al.  Marginal Regression Models for Multivariate Failure Time Data , 1998 .

[4]  S. Rachev Handbook of heavy tailed distributions in finance , 2003 .

[5]  Checking the marginal Cox model for correlated failure time data , 1996 .

[6]  P. Embrechts,et al.  Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management , 2003 .

[7]  P. Hougaard Survival models for heterogeneous populations derived from stable distributions , 1986 .

[8]  Odd O. Aalen,et al.  Modelling Heterogeneity in Survival Analysis by the Compound Poisson Distribution , 1992 .

[9]  Torben Martinussen,et al.  Dynamic Regression Models for Survival Data , 2006 .

[10]  Goele Massonnet Contributions to frailty and copula modelling with applications to clinical trials and dairy cows data , 2008 .

[11]  Elisabeth Wreford Andersen Two-Stage Estimation in Copula Models Used in Family Studies , 2005, Lifetime data analysis.

[12]  R. W. Adkinson,et al.  Distribution of clinical mastitis among quarters of the bovine udder. , 1993, Journal of dairy science.

[13]  Paul Janssen,et al.  Frailty Model , 2007, International Encyclopedia of Statistical Science.

[14]  Mei-Jie Zhang,et al.  A Class of Goodness of Fit Tests for a Copula Based on Bivariate Right‐Censored Data , 2005, Biometrical journal. Biometrische Zeitschrift.

[15]  Paul Janssen,et al.  Frailty models and copulas: similarities and differences , 2008 .

[16]  T. Louis,et al.  Inferences on the association parameter in copula models for bivariate survival data. , 1995, Biometrics.

[17]  David V. Glidden,et al.  A Two-Stage Estimator of the Dependence Parameter for the Clayton-Oakes Model , 2000, Lifetime data analysis.

[18]  P. Janssen,et al.  Resampling Plans for Frailty Models , 2006 .

[19]  R. Nelsen An Introduction to Copulas , 1998 .

[20]  Philip Hougaard,et al.  Analysis of Multivariate Survival Data , 2001 .

[21]  James M Robins,et al.  Locally Efficient Estimation of a Multivariate Survival Function in Longitudinal Studies , 2002 .

[22]  Christian Genest,et al.  Copules archimédiennes et families de lois bidimensionnelles dont les marges sont données , 1986 .

[23]  Gunky Kim,et al.  Comparison of semiparametric and parametric methods for estimating copulas , 2007, Comput. Stat. Data Anal..