Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques

Mixed Models based on Likelihood Boosting

Regularization for generalized additive mixed models by likelihood-based boosting.

OBJECTIVE With the emergence of semi- and nonparametric regression the generalized linear mixed model has been extended to account for additive predictors. However, available fitting methods fail in high dimensional settings where many explanatory variables are present. We extend the concept of boosting to generalized additive mixed models and present an appropriate algorithm that uses two diff...

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...

Conditionally Linear Mixed-Effects Models With Latent Variable Covariates

A version of the nonlinear mixed-effects model is presented that allows random effects only on the linear coefficients. Nonlinear parameters are not stochastic. In nonlinear regression, this kind of model has been called conditionally linear. As a mixed-effects model, this structure is more flexible than the popular linear mixed-effects model, while being nearly as straightforward to estimate. ...

Boosting and Maximum Likelihood for Exponential Models