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Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation

Published online by Cambridge University Press:  04 January 2017

Joseph Bafumi ,
Andrew Gelman ,
David K. Park  and
Noah Kaplan
Joseph Bafumi
Affiliation:
Department of Political Science, Columbia University, New York, NY. e-mail: [email protected]
Andrew Gelman
Affiliation:
Department of Statistics and Department of Political Science, Columbia University, New York, NY. e-mail: [email protected], www.stat.columbia.edu/~gelman/
David K. Park
Affiliation:
Department of Political Science, Washington University, St. Louis, MO. e-mail: [email protected]
Noah Kaplan
Affiliation:
Department of Political Science, University of Houston, Houston, TX. e-mail: [email protected]
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Abstract

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Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explicit modeling for outliers. In addition, we explore new ways to usefully display inferences and check model fit.

Type
Research Article
Information
Political Analysis , Volume 13 , Issue 2 , Spring 2005 , pp. 171 - 187
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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