Henderson's method approach to Kernel prediction in partially linear mixed models

Generalized Partially Linear Mixed-effects Models Incorporating Mismeasured Covariates.

In this article we consider a semiparametric generalized mixed-effects model, and propose combining local linear regression, and penalized quasilikelihood and local quasilikelihood techniques to estimate both population and individual parameters and nonparametric curves. The proposed estimators take into account the local correlation structure of the longitudinal data. We establish normality fo...

A semi-parametric Bayesian approach to generalized linear mixed models.

The linear mixed effects model with normal errors is a popular model for the analysis of repeated measures and longitudinal data. The generalized linear model is useful for data that have non-normal errors but where the errors are uncorrelated. A descendant of these two models generates a model for correlated data with non-normal errors, called the generalized linear mixed model (GLMM). Frequen...

An efficient computing strategy for prediction in mixed linear models

After estimation of e3ects from a linear mixed model, it is often useful to form predicted values for certain factor/variate combinations. This process has been well-de5ned for linear models, but the introduction of random e3ects means that a decision has to be made about the inclusion or exclusion of random model terms from the predictions, including the residual error. For spatially correlate...

Distribution-free Prediction Intervals in Mixed Linear Models

This paper considers prediction intervals for a future observation in the context of mixed linear models. For such prediction problems, it is reasonable to assume that the future observation is independent of the current ones. Our approach is distribution-free, that is, we do not assume that the distributions of the random effects and errors are normal or specified up to a finite number of para...

Partially Linear Models