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Seminar on “Constrained inference in mixed models for clustered data”

August 8 @ 2:00 pm - 3:00 pm

Title: Constrained inference in mixed models for clustered data

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, and in such cases, we can
improve the efficiency of a statistical method by incorporating parameter
constraints into the ML estimation and hypothesis testing. In this talk, I
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.

Presenter:
Sanjoy Sinha
Professor
School of Mathematics and Statistics
Carleton University, Ottawa, ON, Canada

Details

Date:
August 8
Time:
2:00 pm - 3:00 pm
Event Category:

Venue

ISRT Seminar Room (3rd floor)
Institute of Statistical Research and Training, University of Dhaka
Dhaka, Please Select 1000 Bangladesh
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Website:
https://www.isrt.ac.bd/