Name
Title | ||
|---|---|---|
Semiparametric double robust and efficient estimation for mean functionals with response missing at random. |
Abstract | ||
|---|---|---|
Under dimension reduction structure, several semiparametric estimators for the mean of missing response are proposed, which can efficiently deal with the dimensionality problem. Specifically, a generalized version of Augmented Inverse Probability Weighting estimator (AIPW) is proposed and its double robustness, estimation consistency and asymptotic efficiency are investigated. A generalized version of Inverse Probability Weighting (IPW) estimator is also introduced. An asymptotic efficiency reduction phenomenon occurs in the sense that the IPW estimator with the true selection probability is asymptotically less efficient than the one with an estimated selection probability. Besides, two partial imputation and two complete imputation estimators are discussed. We further systematically investigate the comparisons among these estimators in theory. Several simulation studies and a real data analysis are conducted for performance examination and illustration. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.csda.2018.07.017 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
Dimension reduction,Double robustness,Inverse probability weighting,Missing at random | Applied mathematics,Inverse probability weighting,Dimensionality reduction,Curse of dimensionality,Robustness (computer science),Imputation (statistics),Missing data,Statistics,Mathematics,Estimator | Journal |
Volume | ISSN | Citations |
128 | 0167-9473 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |