A0919
Title: Local influence analysis for the sliced average third-moment estimation
Authors: Weidong Rao - Yunnan University of Finance and Economics (China)
Xiaofei Liu - Yunnan University of Finance and Economics (China)
Fei Chen - Yunnan University of Finance and Economics (China) [presenting]
Abstract: Sliced average third-moment estimation (SATME) is a typical method for sufficient dimension reduction (SDR) based on high order conditional moment. It is useful, particularly in the scenarios of regression mixtures. However, as SATME uses the third-order conditional moment of the predictors given the response, it may not as robust as some other SDR methods that use lower-order moments, say, sliced inverse regression (SIR) and slice average variance estimation (SAVE). Based on the space displacement function, a local influence analysis framework of SATME is constructed including a statistic of influence assessment for the observations. Furthermore, a data-trimming strategy is suggested based on the above influence assessment. The proposed methodologies solve a typical issue that also exists in some other SDR methods. A real-data analysis and simulations are presented.
