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Theses Doctoral

Mixed Methods for Mixed Models

Dorie, Vincent Joseph

This work bridges the frequentist and Bayesian approaches to mixed models by borrowing the best features from both camps: point estimation procedures are combined with priors to obtain accurate, fast inference while posterior simulation techniques are developed that approximate the likelihood with great precision for the purposes of assessing uncertainty. These allow flexible inferences without the need to rely on expensive Markov chain Monte Carlo simulation techniques. Default priors are developed and evaluated in a variety of simulation and real-world settings with the end result that we propose a new set of standard approaches that yield superior performance at little computational cost.

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More About This Work

Academic Units
Statistics
Thesis Advisors
Gelman, Andrew E.
Degree
Ph.D., Columbia University
Published Here
January 22, 2014
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