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.
Recommended Citation
Maadooliat, Mehdi; Pourahmadi, Mohsen; and Huang, Jianhua Z., "Robust Estimation of the Correlation Matrix of Longitudinal Data" (2013). Mathematics, Statistics and Computer Science Faculty Research and Publications. 120.
https://epublications.marquette.edu/mscs_fac/120
Comments
Accepted version. Statistics and Computing, Vol. 23, No. 1 (January 2013): 17-28. DOI. © 2013 Springer. Used with permission.
Shareable Link. Provided by the Springer Nature SharedIt content-sharing initiative.