Inference on the primary parameter of interest with the aid of dimension reduction estimation

• 成果类型: Article
• 署名作者: Li, Lexin; Zhu, Liping; Zhu, Lixing
• 署名单位: North Carolina State University; Shanghai University of Finance & Economics; Hong Kong Baptist University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2010.00759.x
• 发表日期: 2011
• 页码: 59-80
• 关键词: sliced inverse regression; asymptotics; likelihood

摘要

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.