Robust estimation of the correlation matrix of longitudinal data Academic Article uri icon


  • 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 =DLLD where D is a diagonal matrix proportional to the square roots of the diagonal entries of 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 LD2L using simulations and a real dataset. 2011 Springer Science+Business Media, LLC.

published proceedings


author list (cited authors)

  • Maadooliat, M., Pourahmadi, M., & Huang, J. Z.

citation count

  • 11

complete list of authors

  • Maadooliat, Mehdi||Pourahmadi, Mohsen||Huang, Jianhua Z

publication date

  • January 1, 2013 11:11 AM