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Activity Number: 594 - Methods for Analysis of High-Dimensional Data
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #328457 Presentation
Title: On Post Dimension Reduction Statistical Inference
Author(s): Kyongwon Kim* and Bing Li
Companies: The Pennsylvania State University and The Pennsylvania State University
Keywords: SIR; SAVE; PHD; DR; GMM

Various dimension reduction methods have been developed to estimate central space or central mean space since two decades ago. However, no one has stated about statistical inference of linear regression after estimating effective dimension reduction(e.d.r) subspace. In this paper, we introduce statistical inference about coefficients of regression model after obtaining the basis matrix of estimated central subspace. We present asymptotic distribution of coefficient of regression model derived from calculating estimating equation and generalized method of moments (GMM) after estimating the basis of central subspace by using various dimension reduction methodology and show the parts that estimated central subspace affects variance of coefficients.

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

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