Department of Mathematics
On a dimension reduction regression with covariate adjustment
In this paper, we consider a semiparametric modeling with multi-indices when neither the response nor the predictors can be directly observed and there are distortions from some multiplicative factors. In contrast to the existing methods in which the response distortion deteriorates estimation efficacy even for a simple linear model, the dimension reduction technique presented in this paper interestingly does not have to account for distortion of the response variable. The observed response can be used directly whether distortion is present or not. The resulting dimension reduction estimators are shown to be consistent and asymptotically normal. The results can be employed to test whether the central dimension reduction subspace has been estimated appropriately and whether the components in the basis directions in the space are significant. Thus, the method provides an alternative for determining the structural dimension of the subspace and for variable selection. A simulation study is carried out to assess the performance of the proposed method. The analysis of a real dataset demonstrates the potential usefulness of distortion removal. © 2011 Elsevier Inc.
Central subspace, Covariate-adjusted regression, Dimension reduction
Source Publication Title
Journal of Multivariate Analysis
Zhang, Jun, Li-Ping Zhu, and Li-Xing Zhu. "On a dimension reduction regression with covariate adjustment." Journal of Multivariate Analysis 104.1 (2012): 39-55.