ON POST DIMENSION REDUCTION STATISTICAL INFERENCE
- 주제(키워드) Central subspace , directional regression , estimating equations , generalized method of moment , influence function , sliced inverse regression , Von Mises expansion
- 주제(기타) Statistics & Probability
- 설명문(일반) [Kim, Kyongwon; Li, Bing] Penn State Univ, Dept Stat, University Pk, PA 16802 USA; [Yu, Zhou] East China Normal Univ, Sch Stat, KLATASDS MOE, Shanghai, Peoples R China; [Li, Lexin] Univ Calif Berkeley, Dept Biostat & Epidemiol, Berkeley, CA 94720 USA
- 등재 SCIE, SCOPUS
- 발행기관 INST MATHEMATICAL STATISTICS
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000182495
- 본문언어 영어
- Published As http://dx.doi.org/10.1214/19-AOS1859
초록/요약
The methodologies of sufficient dimension reduction have undergone extensive developments in the past three decades. However, there has been a lack of systematic and rigorous development of post dimension reduction inference, which has seriously hindered its applications. The current common practice is to treat the estimated sufficient predictors as the true predictors and use them as the starting point of the downstream statistical inference. However, this naive inference approach would grossly overestimate the confidence level of an interval, or the power of a test, leading to the distorted results. In this paper, we develop a general and comprehensive framework of post dimension reduction inference, which can accommodate any dimension reduction method and model building method, as long as their corresponding influence functions are available. Within this general framework, we derive the influence functions and present the explicit post reduction formulas for the combinations of numerous dimension reduction and model building methods. We then develop post reduction inference methods for both confidence interval and hypothesis testing. We investigate the finite-sample performance of our procedures by simulations and a real data analysis.
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