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Kernel dimension reduction in regression. (English) Zbl 1168.62049
Summary: We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives directly from the formulation of SDR in terms of the conditional independence of the covariate $X$ from the response $Y$, given the projection of $X$ on the central subspace [cf. {\it K.-C. Li}, J. Am. Stat. Assoc. 86, No. 414, 316--342 (1991; Zbl 0742.62044); and “Regression graphics. Ideas for studying regressions through graphics.” New York: Wiley (1998; Zbl 0903.62001)]. We show that this conditional independence assertion can be characterized in terms of conditional covariance operators on reproducing kernel Hilbert spaces and we show how this characterization leads to an $M$-estimator for the central subspace. The resulting estimator is shown to be consistent under weak conditions; in particular, we do not have to impose linearity or ellipticity conditions of the kinds that are generally invoked for SDR methods. We also present empirical results showing that the new methodology is competitive in practice.

MSC:
62H05Characterization and structure theory (Multivariate analysis)
46N30Applications of functional analysis in probability theory and statistics
62J99Linear statistical inference
62H99Multivariate analysis
62J02General nonlinear regression
65C60Computational problems in statistics
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Full Text: DOI arXiv
References:
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