For analyzing incomplete longitudinal data, there has been recent interest in comparing estimates with and without the use of multiple imputation along with mixed effects model and generalized estimating equations. Empirically, the additional use of multiple imputation generally led to overestimated variances and may yield more heavily biased estimates than the use of last observation carried f...

SUMMARY An estimating equation, which we call the projected partial score, is introduced for longitudinal data analysis. The estimating equation is obtained by projecting the partial likelihood score function onto the vector space spanned by a class of \conditionally linear" estimating equations. We demonstrate that removing certain terms from the projection of the full likelihood score does no...

With clustered event time data, interest most often lies in marginal features such as quantiles or probabilities from the marginal event time distribution or covariate effects on marginal hazard functions. Copula models offer a convenient framework for modeling. We present methods of estimating the baseline marginal distributions, covariate effects, and association parameters for clustered curr...

In longitudinal data analysis, a correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this article, we consider robust variable selection method in a joint mean and covariance model. We propose a set of penalized robust generalized estimating equations to simultaneously estimate the mean regression coefficients, th...

In this paper, new results for estimating the solution of differential and algebraic Lyapunov matrix equations are obtained, and some of the well-known results are generalized.

The author describes the relationship between the extended generalized estimating equations (EGEEs) of Hall & Severini (1998) and various similar methods. He proposes a true extended quasi-likelihood approach for the clustered data case and explores restricted maximum likelihood-like versions of the EGEE and extended quasi-likelihood estimating equations. He also presents simulation results com...

In this paper, we present a generalized estimating equations based estimation approach and a variable selection procedure for single-index models when the observed data are clustered. Unlike the case of independent observations, bias-correction is necessary when general working correlation matrices are used in the estimating equations. Our variable selection procedure based on smooth-threshold ...