Linear mixed-effects models play a fundamental role in statistical methodology. A variety of Markov chain Monte Carlo (MCMC) algorithms exist for fitting these models, but they are inefficient massive data settings because every iteration any such MCMC algorithm passes through the full data. Many divide-and-conquer methods have been proposed to solve this problem, lack theoretical guarantees, i...

STUDIES OF PARACRYSTAL FORMATION BY COLUMN PURIFIED LIGHT MEROMYOSIN (LMM) PREPARED IN A VARIETY OF WAYS LED TO THE FOLLOWING CONCLUSIONS: (a) different portions of the myosin rod may be coded for different stagger relationships. This was concluded from observations that paracrystals with different axial repeat periodicities could be obtained either with LMM framents of different lengths prepar...

Equivalent asymptotic procedures can yield conflicting inference when applied to the same dataset (Aubin & Cordeiro, 2000). Among the class of likelihood ratio tests, modified likelihood ratio tests (e.g., Bartlett, 1937) have been proposed and have documented improved performances over classical procedures. Modified F tests have also been proposed (e.g., Kenward & Roger, 1997), and have been r...

Longitudinal studies are increasingly common in psychological and social sciences research. In these studies, subjects are measured repeatedly across time and interest often focuses on characterizing their growth or development across time. Mixed-effects regression models (MRMs) have become the method of choice for modeling of longitudinal data; variants of MRMs have been developed under a vari...

Generalized linear mixed models (GLMM) form a very general class of random effects models for discrete and continuous responses in the exponential family. They are useful in a variety of applications. The traditional likelihood approach for GLMM usually involves high dimensional integrations which are computationally intensive. In this work, we investigate the case of binary outcomes analyzed u...

The generalized linear mixed model (GLIMMIX) provides a powerful technique to model correlated outcomes with different types of distributions. The model can now be easily implemented with SAS PROC GLIMMIX in version 9.1. For binary outcomes, linearization methods of penalized quasi-likelihood (PQL) or marginal quasi-likelihood (MQL) provide relatively accurate variance estimates for fixed effec...

analysis of variance (ANOVA): one-way nested, hierarchical two-way linear model fixed, random, and mixed models factorial arrangement of treatments variance components sum of squares, mean square, expected mean square sampling fraction, fixed effects, random effects simple effects, main effects, interaction effects, additive effects analysis of covariance, covariate, concomitant variable data t...

We describe our recent work on mixed effects models for right-censored data. Vaida and Xu (2000) provided a general framework for handling random effects in proportional hazards (PH) regression, in a way similar to the linear, non-linear and generalized linear mixed effects models that allow random effects of arbitrary covariates. This general framework includes the frailty models as a special ...

We introduce a flexible marginal modelling approach for statistical inference for clustered/longitudinal data under minimal assumptions. This estimated estimating equations (EEE) approach is semiparametric and the proposed models are fitted by quasi-likelihood regression, where the unknown marginal means are a function of the fixed-effects linear predictor with unknown smooth link, and variance...

Parametric nonlinear mixed effects models (NLMEs) are now widely used in biometrical studies, especially in pharmacokinetics research and HIV dynamics models, due to, among other aspects, the computational advances achieved during the last years. However, this kind of models may not be flexible enough for complex longitudinal data analysis. Semiparametric NLMEs (SNMMs) have been proposed by Ke ...