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• W2134767337 abstract "Summary 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 bias–variance 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." @default.
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• W2134767337 date "2012-05-31" @default.
• W2134767337 modified "2023-10-06" @default.
• W2134767337 title "A Sparse Factor Analytic Probit Model for Congressional Voting Patterns" @default.
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• W2134767337 doi "https://doi.org/10.1111/j.1467-9876.2012.01044.x" @default.
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