Model checking for partially linear models with missing responses at random
In this paper, we investigate the model checking problem for partial linear model while some responses are missing at random. By imputation and marginal inverse probability weighted methods, two completed data sets are constructed.Based on the two completed data sets, we build two empirical process-based tests for examining the adequacy of partial linearity of the model. The asymptotic distributions of the test statistics under the null hypothesis and local alternative hypotheses are obtained respectively. A re-sampling approach is applied to obtain the approximation to the null distributions of the test statistics. Simulation results show that the proposed tests work well and both proposed methods have better finity sample properties compared with the CC analysis which discards all the subjects with missing data.
Model checking Response missing at random Imputation Inverse probability weighting Empirical process Re-sampling
Zhihua Sun Qihua Wang Pengjie Dai
Department of Mathematics, Graduate School, Chinese Academy of Sciences Beijing 100049, China; Acade Academy of Mathematics and Systems Science, Chinese Academy of Sciences Beijing 100080, China;Depart Academy of Mathematics and Systems Science, Chinese Academy of Sciences Beijing 100080, China
国际会议
北京
英文
72-104
2008-08-10（万方平台首次上网日期，不代表论文的发表时间）