Nonlinear mixed-effects scalar-on-function models and variable selection

Variable Selection in Function-on-Scalar Regression.

For regression models with functional responses and scalar predictors, it is common for the number of predictors to be large. Despite this, few methods for variable selection exist for function-on-scalar models, and none account for the inherent correlation of residual curves in such models. By expanding the coefficient functions using a B-spline basis, we pose the function-on-scalar model as a...

Variable Selection in Linear Mixed Effects Models.

This paper is concerned with the selection and estimation of fixed and random effects in linear mixed effects models. We propose a class of nonconcave penalized profile likelihood methods for selecting and estimating important fixed effects. To overcome the difficulty of unknown covariance matrix of random effects, we propose to use a proxy matrix in the penalized profile likelihood. We establi...

Variable Selection in Linear Mixed Effects Models By

This paper is concerned with the selection and estimation of fixed and random effects in linear mixed effects models. We propose a class of nonconcave penalized profile likelihood methods for selecting and estimating important fixed effects. To overcome the difficulty of unknown covariance matrix of random effects, we propose to use a proxy matrix in the penalized profile likelihood. We establi...

Bayesian variable selection in linear mixed effects models

Bayesian Variable Selection for Probit Mixed Models

In computational biology, gene expression datasets are characterized by very few individual samples compared to a large number of measurments per sample. Thus, it is appealing to merge these datasets in order to increase the number of observations and diversify the data, allowing a more reliable selection of genes relevant to the biological problem. This necessitates the introduction of the dat...