TY - JOUR
T1 - A note on robust kernel inverse regression
AU - Dong, Yuexiao
AU - Yu, Zhou
AU - Sun, Yizhi
PY - 2013
Y1 - 2013
N2 - As a useful tool for sufficient dimension reduction, kernel inverse regression (KIR) can effectively relieve the curse of dimensionality by finding linear combinations of the predictor that contain all the relevant information for regression. However, KIR is sensitive to outliers, and will fail when the predictor distribution is heavy-tailed. In this paper, we discuss robust variations of KIR that do not have such limitations. The effectiveness of our proposed methods is demonstrated via simulation studies and an application to the automobile price data.
AB - As a useful tool for sufficient dimension reduction, kernel inverse regression (KIR) can effectively relieve the curse of dimensionality by finding linear combinations of the predictor that contain all the relevant information for regression. However, KIR is sensitive to outliers, and will fail when the predictor distribution is heavy-tailed. In this paper, we discuss robust variations of KIR that do not have such limitations. The effectiveness of our proposed methods is demonstrated via simulation studies and an application to the automobile price data.
KW - Elliptical distribution
KW - Kernel inverse regression
KW - Permutation test
KW - Sufficient dimension reduction
UR - https://www.scopus.com/pages/publications/84876336176
U2 - 10.4310/SII.2013.v6.n1.a5
DO - 10.4310/SII.2013.v6.n1.a5
M3 - 文章
AN - SCOPUS:84876336176
SN - 1938-7989
VL - 6
SP - 45
EP - 52
JO - Statistics and its Interface
JF - Statistics and its Interface
IS - 1
ER -