For the mixed-effects models with two variance components which is often adopted for analyzing longitudinal data, we establish some necessary and sufficient condition for equality of the analysis of variance estimate and the spectral decomposition estimate of variance components. Thus when this condition is satisfied, both estimates share some statistical properties. Two practical examples sati...

Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the context of generalized linear mixed models used to analyze clustered or longitu...

A complex trait like crop yield is determined by its component traits. Multivariable conditional analysis in a general mixed linear model is helpful in dissecting the gene expression for the complex trait due to different effects, such as environment, genotype, and genotype× environment interaction. A recursive approach is presented for constructing a new randomvector that can be equivalently u...

Linear models for uncorrelated data have well established measures to gauge the influence of one or more observations on the analysis. For such models, closed-form update expressions allow efficient computations without refitting the model. When similar notions of statistical influence are applied to mixed models, things are more complicated. Removing data points affects fixed effects and covar...

We consider N independent stochastic processes (Xj(t), t ∈ [0, T ]), j = 1, . . . , N , defined by a one-dimensional stochastic differential equation with coefficients depending on a random variable φj and study the nonparametric estimation of the density of the random effect φj in two kinds of mixed models. A multiplicative random effect and an additive random effect are successively considere...

This paper improves and extends the two-step penalized iterative estimation procedure for the linear mixed effect model (LMM) by explicitly penalizing the off-diagonal components of the covariance matrix of random effects. To explicitly penalize the off-diagonal terms in the covariance matrix of random effects, glasso is incorporated in the penalized LMM approach. The paper also provides theore...

Linear models for uncorrelated data have well established measures to gauge the influence of one or more observations on the analysis. For such models, closed-form update expressions allow efficient computations without refitting the model. When similar notions of statistical influence are applied to mixed models, things are more complicated. Removing data points affects fixed effects and covar...

Inference in Generalized linear mixed models with multivariate random effects is often made cumbersome by the high-dimensional intractable integrals involved in the marginal likelihood. An inferentialmethodology based on themarginal pairwise likelihood approach is proposed. This method belonging to the broad class of composite likelihood involves marginal pairs probabilities of the responses wh...

Using forest inventory data and Landsat ETM+ data, linear fixed-effects models and linear mixed-effects models are developed based on the allometric growth model. The surface area of the normalized difference vegetation index (NDVIsa) is developed from the triangulated irregular network (TIN) with the aid of image-processing and the three-dimensional analysis extensions of Environmental Systems...