Adaptive Fitting of Linear Mixed-Effects Models with Correlated Random-effects.

ABSTRACT: Linear mixed-effects model has been widely used in longitudinal data analyses. In practice, the fitting algorithm can fail to converge due to boundary issues of the estimated random-effects covariance matrix G, i.e., being near-singular, non-positive definite, or both. Current available algorithms are not computationally optimal because the condition number of matrix G is unnecessarily increased when the random-effects correlation estimate is not zero. We propose an adaptive fitting (AF) algorithm using an optimal linear transformation of the random-effects design matrix. It is a data-driven adaptive procedure, aiming at reducing subsequent random-effects correlation estimates down to zero in the optimal transformed estimation space. Simulations show that AF significantly improves the conv

SUBMITTER: Zhang G 

PROVIDER: S-EPMC3836449 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature