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Partial central subspace and sliced average variance estimation. (English) Zbl 1156.62032
Summary: Sliced average variance estimation is one of many methods for estimating the central subspace. It was shown to be more comprehensive than sliced inverse regression in the sense that it consistently estimates the central subspace under mild conditions while sliced inverse regression may estimate only a proper subset of the central subspace. We extend this method to regressions with qualitative predictors. We also provide tests of dimension and a marginal coordinate hypothesis test. We apply the method to a data set concerning lakes infested by Eurasian Watermilfoil, and compare this new method to the partial inverse regression estimator.

62G08 Nonparametric regression and quantile regression
62J99 Linear inference, regression
62P12 Applications of statistics to environmental and related topics
62E20 Asymptotic distribution theory in statistics
62H12 Estimation in multivariate analysis
Full Text: DOI
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