Kavli Affiliate: Wei Gao
| First 5 Authors: Zheng Li, Chong Ding, Wei Gao, ,
| Summary:
Sufficient dimension reduction (SDR) is a valuable approach for handling
high-dimensional data. Outer Product Gradient (OPG) is an popular approach.
However, because of focusing the mean regression function, OPG may ignore some
directions of central subspace (CS) when the distribution of errors is
symmetric about zero. The mode of a distribution can provide an important
summary of data. A Local Modal OPG (LMOPG) and its algorithm through mode
regression are proposed to estimate the basis of CS with skew errors
distribution. The estimator shows the consistent and asymptotic normal
distribution under some mild conditions. Monte Carlo simulation is used to
evaluate the performance and demonstrate the efficiency and robustness of the
proposed method.
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