A sparse factor analytic probit model for congressional voting patterns

作者：Hahn P Richard; Carvalho Carlos M; Scott James G^*

来源：Journal of the Royal Statistical Society - Series C: Applied Statistics , 2012, 61: 619-635.

DOI：10.1111/j.1467-9876.2012.01044.x

• Covariance estimation
• Factor models
• Multivariate probit models
• Voting patterns

. The paper adapts sparse factor models for exploring covariation in multivariate binary data, with an application to measuring latent factors in US Congressional roll-call voting patterns. This straightforward modification provides two advantages over traditional factor analysis of binary data. First, a sparsity prior can be used to assess the evidence that a given factor loading may be exactly 0, realizing a principled unification of exploratory and confirmatory factory analysis. Second, incorporating sparsity into existing factor analytic probit models effects a favourable biasvariance trade-off in estimating the covariance matrix of the multivariate Gaussian latent variables. Posterior summaries from this model applied to the roll-call data provide novel metrics of partisanship of a given Senate.

• 出版日期2012