EFFICIENT ESTIMATION IN SUFFICIENT DIMENSION REDUCTION.
We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem of identifying the central subspace to the problem of estimating a finite dimensional parameter in a semiparametric model. This conversion allows us to derive an efficient estimator which reaches the optimal semiparametric efficiency bound. The resulting efficient estimator can exhaustively estimate the central subspace without imposing any distributional assumptions. Our proposed efficient estimation also provides a possibility for making inference of parameters that uniquely identify the central subspace. We conduct simulation studies and a real data analysis to demonstrate the finite sample performance in comparison with several existing methods.
Department of Statistics, Texas A&M University, 3143 TAMU, College Station, Texas 77843-3143, United States.
Yanyuan
Y
Ma
Liping
L
Zhu
Central subspace; dimension reduction; estimating equations; semiparametric efficiency; sliced inverse regression