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Asymptotic Test for Dimensionality in Sliced Inverse Regression

被引：0
|
作者：
Park, Chongsun [1 ]
Kwak, Jae Guen [1 ]
机构：
[1] Sungkyunkwan Univ, Dept Stat, 3-53 Myungryun Dong, Seoul 110745, South Korea
来源：
KOREAN JOURNAL OF APPLIED STATISTICS | 2005年 / 18卷 / 02期
关键词：
Sliced inverse regression; Dimension reduction; Latent variable model; Asymptotic test;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.
引用
收藏
页码：381 / 393
页数：13
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