Principal component analysis of binary data by iterated singular value decomposition
نویسنده
چکیده
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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ورودعنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 50 شماره
صفحات -
تاریخ انتشار 2006