Document Type

Article

Language

eng

Format of Original

12 p.

Publication Date

1-2013

Publisher

Springer

Source Publication

Statistics and Computing

Source ISSN

0960-3174

Original Item ID

doi: 10.1007/s11222-011-9284-6

Abstract

We propose a double-robust procedure for modeling the correlation matrix of a longitudinal dataset. It is based on an alternative Cholesky decomposition of the form Σ=DLL D where D is a diagonal matrix proportional to the square roots of the diagonal entries of Σ and L is a unit lower-triangular matrix determining solely the correlation matrix. The first robustness is with respect to model misspecification for the innovation variances in D, and the second is robustness to outliers in the data. The latter is handled using heavy-tailed multivariate t-distributions with unknown degrees of freedom. We develop a Fisher scoring algorithm for computing the maximum likelihood estimator of the parameters when the nonredundant and unconstrained entries of (L,D) are modeled parsimoniously using covariates. We compare our results with those based on the modified Cholesky decomposition of the form LD 2 L using simulations and a real dataset.

Comments

Accepted version. Statistics and Computing, Vol. 23, No. 1 (January 2013): 17-28. DOI. © Springer 2013. Used with permission.

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