Department of Mathematics
Inference on the primary parameter of interest with the aid of dimension reduction estimation
As high dimensional data become routinely available in applied sciences, sufficient dimension reduction has been widely employed and its research has received considerable attention. However, with the majority of sufficient dimension reduction methodology focusing on the dimension reduction step, complete analysis and inference after dimension reduction have yet to receive much attention. We couple the strategy of sufficient dimension reduction with a flexible semiparametric model. We concentrate on inference with respect to the primary variables of interest, and we employ sufficient dimension reduction to bring down the dimension of the regression effectively. Extensive simulations demonstrate the efficacy of the method proposed, and a real data analysis is presented for illustration. © 2010 Royal Statistical Society.
Central partial mean subspace, Dimension reduction, Partial ordinary least squares, Partially linear single-index model
Source Publication Title
Journal of the Royal Statistical Society: Series B
Royal Statistical Society
Link to Publisher's Edition
Li, Lexin, Liping Zhu, and Lixing Zhu. "Inference on the primary parameter of interest with the aid of dimension reduction estimation." Journal of the Royal Statistical Society: Series B 73.1 (2011): 59-80.