Direction estimation in single-index models via distance covariance

Wenhui Sheng and Xiangrong Yin

Journal of Multivariate Analysis, 2013, vol. 122, issue C, 148-161

Abstract: We introduce a new method for estimating the direction in single-index models via distance covariance. Our method keeps model-free advantage as a dimension reduction approach. In addition, no smoothing technique is needed, which enables our method to work efficiently when many predictors are discrete or categorical. Under regularity conditions, we show that our estimator is root-n consistent and asymptotically normal. We compare the performance of our method with some dimension reduction methods and the single-index estimation method by simulations and show that our method is very competitive and robust across a number of models. Finally, we analyze the UCI Adult Data Set to demonstrate the efficacy of our method.

Keywords: Brownian distance covariance; Central subspace; Distance covariance; Single-index model; Sufficient dimension reduction (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.jmva.2013.07.003

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