Inference for variance components in linear mixed models of ANOVA type, including estimation and testing, has been investigated when the number of fixed effects is fixed. However, for high-dimensional data, this number is large and would be regarded as a divergent value as the sample size goes to infinity. In this paper, existing tests are extended to handle this problem with a sparse model str...

The search for conditions for the consistency of maximum likelihood estimators in nonlinear mixed effects models is difficult due to the fact that, in general, the likelihood can only be expressed as an integral over the random effects. For repeated measurements or clustered data, we focus on asymptotic theory for the maximum likelihood estimator for the case where the cluster sizes go to infin...

Linear Mixed Effects (LME) models have been widely applied in clustered data analysis many areas including marketing research, clinical trials, and biomedical studies. Inference can be conducted using maximum likelihood approach if assuming Normal distributions on the random effects. However, applications of economy, business medicine, it is often essential to impose constraints regression para...

Ordinary differential equations (ODEs) are popular tools for modeling complicated dynamic systems in many areas. When multiple replicates of measurements are available for the dynamic process, it is of great interest to estimate mixed-effects in the ODE model for the process. We propose a semiparametric method to estimate mixed-effects ODE models. Rather than using the ODE numeric solution dire...

A common analysis objective is estimation of a realized random effect. The parameter underlying such an effect is usually defined as an average response of a realized unit, such as a cluster mean, domain mean, small area mean, or subject effect. The effects are called random effects since their occurrence is the result of some (actual or assumed) random sampling process. In mixed models, random...

An affiliation network is a particular kind of two-mode social network that consists of a set of ‘actors’ and a set of ‘events’ where ties indicate an actor’s participation in an event. While event affiliations are fundamental in defining the social identity of individuals, statistical methods for studying affiliation networks are less well developed than methods for studying one-mode, or actor...