TY - JOUR
T1 - Dimension reduction with missing response at random
AU - Guo, Xu
AU - Wang, Tao
AU - Xu, Wangli
AU - Zhu, Lixing
N1 - Funding Information:
The last author’s research described here was supported by a grant from the University Grants Council of Hong Kong , Hong Kong and the third author was supported by National Natural Science Foundation of China (No. 11071253 ). The authors thank Dr. Xiaobo Ding for kindly providing computation codes in the simulation studies and Dr. Zonghui Hu for generously providing the data in the real example. The authors thank the Editor, the Associate Editor and two referees for their constructive comments and suggestions which led to an improvement of an early manuscript.
PY - 2014/1
Y1 - 2014/1
N2 - When there are many predictors, how to efficiently impute responses missing at random is an important problem to deal with for regression analysis because this missing mechanism, unlike missing completely at random, is highly related to high-dimensional predictor vectors. In sufficient dimension reduction framework, the fusion-refinement (FR) method in the literature is a promising approach. To make estimation more accurate and efficient, two methods are suggested in this paper. Among them, one method uses the observed data to help on missing data generation, and the other one is an ad hoc approach that mainly reduces the dimension in the nonparametric smoothing in data generation. A data-adaptive synthesization of these two methods is also developed. Simulations are conducted to examine their performance and a HIV clinical trial dataset is analyzed for illustration.
AB - When there are many predictors, how to efficiently impute responses missing at random is an important problem to deal with for regression analysis because this missing mechanism, unlike missing completely at random, is highly related to high-dimensional predictor vectors. In sufficient dimension reduction framework, the fusion-refinement (FR) method in the literature is a promising approach. To make estimation more accurate and efficient, two methods are suggested in this paper. Among them, one method uses the observed data to help on missing data generation, and the other one is an ad hoc approach that mainly reduces the dimension in the nonparametric smoothing in data generation. A data-adaptive synthesization of these two methods is also developed. Simulations are conducted to examine their performance and a HIV clinical trial dataset is analyzed for illustration.
KW - Central subspace
KW - Data-adaptive synthesization
KW - Missing recovery
KW - Missing response at random Multiple imputation
UR - http://www.scopus.com/inward/record.url?scp=84884274207&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2013.08.001
DO - 10.1016/j.csda.2013.08.001
M3 - Journal article
AN - SCOPUS:84884274207
SN - 0167-9473
VL - 69
SP - 228
EP - 242
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -
