Principal Component Analysis of Binary Data. Applications to Roll-call Analysis
نویسنده
چکیده
We compute the maximum likelihood estimates of a principal component analysis on the logit or probit scalem using a majorization algorithm that computes a sequence of singular value decompositions. The technique is applied to 2001 house and senate roll call data and compared with other techniques for roll call analysis.
منابع مشابه
Principal component analysis of binary data by iterated singular value decomposition
The maximum likelihood estimates of a principal component analysis on the logit or probit scale are computed using majorization algorithms that iterate a sequence of weighted or unweighted singular value decompositions. The relation with similar methods in item response theory, roll call analysis, and binary choice analysis is discussed. The technique is applied to 2001 US House roll call data.
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