Department of Mathematics
Model checking for parametric regressions with response missing at random
© 2014, The Institute of Statistical Mathematics, Tokyo.This paper aims at investigating model checking for parametric models with response missing at random which is a more general missing mechanism than missing completely at random. Different from existing approaches, two tests have normal distributions as the limiting null distributions no matter whether the inverse probability weight is estimated parametrically or nonparametrically. Thus, p values can be easily determined. This observation shows that slow convergence rate of nonparametric estimation does not have significant effect on the asymptotic behaviors of the tests although it may have impact in finite sample scenarios. The tests can detect the alternatives distinct from the null hypothesis at a nonparametric rate which is an optimal rate for locally smoothing-based methods in this area. Simulation study is carried out to examine the performance of the tests. The tests are also applied to analyze a data set on monozygotic twins for illustration.
Inverse probability weight, Model checking, Response missing at random
Source Publication Title
Annals of the Institute of Statistical Mathematics
Link to Publisher's Edition
Guo, Xu, Wangli Xu, and Lixing Zhu. "Model checking for parametric regressions with response missing at random." Annals of the Institute of Statistical Mathematics 67.2 (2015): 229-259.