Bayesian Analysis of Individual Choice BehaviorWith Aggregate Data

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The statistical analysis of individual choice behavior can be hampered by aggregate information. When a choice response variable is fully observed, individual choice behavior is typically studied with generalized linear models with the logit or probit link function. The individual choice response variable is, however, often available in an aggregate form because of data confidentiality, privacy protection, and a limited database quota. Then the generalized linear models are no longer directly applicable to such aggregate data. In this article, we confront such an aggregate data problem by using the method of data augmentation, where latent individual choice responses are augmented while satisfying a constraint on their aggregate sum. To do so, we devise the novel choice-wise sampling algorithm for a generalized linear model with aggregate binary responses. The proposed algorithm is applied to the 2006 Pennsylvania gubernatorial election data, where only aggregate votes cast for each candidate are available because of the privacy protection of voters. This article has supplementary material online.

Original languageEnglish
Pages (from-to)158-173
Number of pages16
JournalJournal of Computational and Graphical Statistics
Volume20
Issue number1
DOIs
Publication statusPublished - 2011 Mar 1

Fingerprint

Bayesian Analysis
Generalized Linear Model
Privacy Protection
Data Augmentation
Probit
Binary Response
Link Function
Logit
Confidentiality
Vote
Elections
Bayesian analysis
Aggregate data
Statistical Analysis
Generalized linear model
Choice behavior
Privacy

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics

Cite this

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Bayesian Analysis of Individual Choice BehaviorWith Aggregate Data. / Park, Taeyoung.

In: Journal of Computational and Graphical Statistics, Vol. 20, No. 1, 01.03.2011, p. 158-173.

Research output: Contribution to journalArticle

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