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Estimating Central Subspaces via Inverse Third Moments
Xiangrong Yin and R. Dennis Cook
Vol. 90, No. 1 (Mar., 2003), pp. 113-125
Stable URL: http://www.jstor.org/stable/30042023
Page Count: 13
You can always find the topics here!Topics: Statistical variance, Statism, Matrices, Mathematical vectors, Diabetes, Population estimates, Linear regression, Dimensionality reduction, Statistical estimation, Estimation methods
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Modern graphical tools have enhanced our ability to learn many things from data directly. In recent years, dimension reduction has proven to be an effective tool for generating low-dimensional summary plots without appreciable loss of information. Some well-known inverse regression methods for dimension reduction such as sliced inverse regression (Li, 1991) and sliced average variance estimation (Cook & Weisberg, 1991) have been developed to estimate summary plots for regression and discriminant analysis. In this paper, we suggest a new method that makes use of inverse third moments. This method can find structure beyond that found by sliced inverse regression and sliced average variance estimation, particularly regression mixtures. Illustrative examples are presented.
Biometrika © 2003 Biometrika Trust