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January 24, 2010 | History

Assessing the impact of measurement error in multilevel models via MCMC methods 1 edition

Assessing the impact of measurement error in multilevel models via MCM ...
Anjali Mazumder

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Assessing the impact of measurement error in multilevel models via MCMC methods.

Published 2005 .
Written in English.

About the Book

The aim of this thesis is to integrate three areas of statistical research---multilevel modeling, Markov chain Monte Carlo (MCMC) methods, and measurement error. Three distinct types of multilevel models are considered: random-intercepts models, random-slopes models, and models with complex variation at level-1. These models are fitted using MCMC and maximum likelihood methods and the fits are compared. Finally, the effects of measurement error in predictors are assessed for different reliabilities and adjusted for using MCMC methods. The results indicate that MCMC samplers with non-informative priors produce similar results to maximum likelihood estimates and adjust for measurement error in predictors effectively. In general, MCMC methods give smaller standard errors, making inferential statements more powerful, and facilitate the use of additional information to guide the measurement and regression process. The simulations were performed using S-plus and the multilevel model problems were formulated and solved using MLwiN.

Edition Notes

Source: Masters Abstracts International, Volume: 44-01, page: 0379.

Thesis (M.A.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.

ROBARTS MICROTEXT copy on microfiche.

The Physical Object

107 leaves.
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History Created December 11, 2009 · 2 revisions Download catalog record: RDF / JSON

January 24, 2010 Edited by WorkBot add more information to works
December 11, 2009 Created by WorkBot add works page