B0524
Title: Constrained inference in mixed models for clustered data
Authors: Sanjoy Sinha - Carleton University (Canada) [presenting]
Abstract: Mixed models are commonly used for analyzing clustered data, including longitudinal data and repeated measurements. Unrestricted full maximum likelihood (ML) methods have been extensively studied in the literature for analyzing generalized, linear, and mixed models. However, constraints or parameter orderings may occur in practice. In such cases, we can improve the efficiency of a statistical method by incorporating parameter constraints into ML estimation and hypothesis testing. We will discuss constrained inference with generalized linear mixed models (GLMMs) under linear inequality constraints. Methods will be assessed using both Monte Carlo simulations and actual survey data from a health study.
