Principal component analysis of binary data by iterated singular value decomposition

Authors:
Jan de Leeuw
Affiliations:
Department of Statistics, University of California, Los Angeles, 8130 Math Sciences Blvd., Los Angeles, CA 90095-1554, USA
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

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.