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Activity Number: 89 - SPEED: Survey Methods, Transportation Studies, SocioEconomics, and General Statistical Methods Part 2
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 5:05 PM to 5:50 PM
Sponsor: Transportation Statistics Interest Group
Abstract #307925
Title: Estimating Generalized Linear Models with the Pseudo-Marginal Metropolis-Hastings Algorithm
Author(s): Taylor Brown* and Tim McMurry
Companies: University of Virginia and University of Virginia School of Medicine
Keywords: Markov chain Monte Carlo; missing data; generalized linear model

The missing data issue often complicates the task of estimating generalized linear models (GLMs). We describe why the pseudo-marginal Metropolis-Hastings algorithm, used in this setting, is an effective strategy for parameter estimation. The flexibility of this approach allows for general priors to be put on both the missing covariates and the parameters, uses all of the available data, can easily be extended to handle a nonignorable missing-data mechanism, and is still asymptotically exact like most other Markov chain Monte Carlo techniques. We discuss computing strategies, conduct a simulation study demonstrating how standard errors change as a function of percent missingness, and we use our approach on a "real-world" data set to describe how a collection of variables influence the car crash outcomes.

Authors who are presenting talks have a * after their name.

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