Abstract
We consider a nonlinear censored regression problem with a vector of predictors. With censoring, high-dimensional regression analysis becomes much more complicated. Since censoring can cause severe bias in estimation, modification to adjust such bias is needed to be made. Based on the weight adjustment, we develop the modification of sliced average variance estimation for estimating the lifetime central subspace without requiring a prespecified parametric model. Our proposed method preserves as much regression information as possible. Simulation results are reported and comparisons are made with the sliced inverse regression of Li et al. (Citation1999).
Mathematics Subject Classification:
Acknowledgment
We are grateful to the Editor and two referees for helpful suggestions. We thank Dr. Chun-houh Chen for providing the program of modified SIR.