Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145800 | Journal of Multivariate Analysis | 2013 | 14 Pages |
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-nn 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
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Wenhui Sheng, Xiangrong Yin,