Multivariate Prediction with Nonlinear Principal Components Analysis: Theory

Multivariate Prediction with Nonlinear Principal Components Analysis: Theory We propose the notion of multivariate predictability as a measure of goodness-of-fit in data reduction techniques which are useful for visualizing and screening data. For quantitative variables this leads to the usual sums-of-squares and variance accounted for criteria. For categorical variables we show how to predict the category-levels of all variables associated with every point (case). The proportion of predictions which agree with the true categories gives the measure of fit. The ideas are very general; as an illustration we use nonlinear principal components analysis (NLPCA) in association with ordered categorical variables. A detailed example using data from the International Social Survey Program (ISSP) will be given in Blasius and Gower (quality and quantity, 39, to appear). It will be shown that the predictability criterion suggests that the fits are rather better than is indicated by “percentage of variance accounted for”. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Multivariate Prediction with Nonlinear Principal Components Analysis: Theory

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2005 by Springer
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-005-3005-1
Publisher site
See Article on Publisher Site

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