Online Program Home
My Program

Abstract Details

Activity Number: 241 - Estimation Challenges and New Approaches
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #301853
Title: On Post Dimension Reduction Statistical Inference
Author(s): Kyongwon Kim*
Companies: The Pennsylvania State University
Keywords: Central Subspace; Directional Regression; Estimating Equations; Generalized Method of Moment; Influence Function; Von-Mises Expansion
Abstract:

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


Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program