Efficient Bayesian analysis of multivariate aggregate choices

Taeyoung Park; Seonghyun Jeong

doi:10.1080/00949655.2014.973411

Efficient Bayesian analysis of multivariate aggregate choices

Taeyoung Park, Seonghyun Jeong

Department of Applied Statistics

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

In estimating individual choice behaviour using multivariate aggregate choice data, the method of data augmentation requires the imputation of individual choices given their partial sums. This article proposes and develops an efficient procedure of simulating multivariate individual choices given their aggregate sums, capitalizing on a sequence of auxiliary distributions. In this framework, a joint distribution of multiple binary vectors given their sums is approximated as a sequence of conditional Bernoulli distributions. The proposed approach is evaluated through a simulation study and is applied to a political science study.

Original language	English
Pages (from-to)	3352-3366
Number of pages	15
Journal	Journal of Statistical Computation and Simulation
Volume	85
Issue number	16
DOIs	https://doi.org/10.1080/00949655.2014.973411
Publication status	Published - 2015 Nov 2

Bibliographical note

Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0011866).

Publisher Copyright:
© 2014 Taylor & Francis.

All Science Journal Classification (ASJC) codes

Statistics and Probability
Modelling and Simulation
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1080/00949655.2014.973411

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AB - In estimating individual choice behaviour using multivariate aggregate choice data, the method of data augmentation requires the imputation of individual choices given their partial sums. This article proposes and develops an efficient procedure of simulating multivariate individual choices given their aggregate sums, capitalizing on a sequence of auxiliary distributions. In this framework, a joint distribution of multiple binary vectors given their sums is approximated as a sequence of conditional Bernoulli distributions. The proposed approach is evaluated through a simulation study and is applied to a political science study.

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Efficient Bayesian analysis of multivariate aggregate choices

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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