Likelihood identifiability and parameter estimation with nonignorable missing data
文献类型：期刊论文
| 作者 | ZHENG, Siming1,2; ZHANG, Juan3; ZHOU, Yong4,5 |
| 刊名 | CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
 |
| 出版日期 | 2022-05-27 |
| 页码 | 20 |
| 关键词 | Equivalent asymptotic efficiency exponentially tilting generalized method of moments identifiability nonignorable missingness |
| ISSN号 | 0319-5724 |
| DOI | 10.1002/cjs.11704 |
| 英文摘要 | We identify sufficient conditions to resolve the identification problem under nonignorable missingness, especially the identifiability of the observed likelihood when some of the covariate values are missing not at random, or, simultaneously, the response is also missing not at random. It is more difficult to tackle these cases than the nonignorable nonresponse case, and, to the best of our knowledge, the simultaneously missing case has never been discussed before. Under these conditions, we propose some parameter estimation methods. As an illustration, when some of the covariate values are missing not at random, we adopt a semiparametric logistic model with a tilting parameter to model the missingness mechanism and use an imputed estimating equation based on the generalized method of moments to estimate the parameters of interest and the tilting parameter simultaneously. This approach avoids the requirement for other independent surveys or a validation sample to estimate the unknown tilting parameter. The asymptotic properties of our proposed estimators are derived, and the proofs can be modified to show that our methods of estimation, which are based on inverse probability weighting, augmented inverse probability weighting, and estimating equation projection, have the same asymptotic efficiency when the tilting parameter is either known or unknown but estimated by some other method. In simulation studies, we compare our methods with various alternative approaches and find that our methods are more robust and effective. |
| 资助项目 | National Key Research and Development Program of China[2021YFA1000100] ; National Key Research and Development Program of China[2021YFA1000101] ; State Key Program of National Natural Science Foundation of China[71931004] |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000800633000001 |
| 出版者 | WILEY |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/61408]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | ZHOU, Yong |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 2.City Univ Hong Kong, Dept Management Sci, Hong Kong, Peoples R China 3.Capital Univ Econ & Business, Sch Stat, Beijing, Peoples R China 4.Key Lab Adv Theory & Applicat Stat & Data Sci, MOE, Shanghai, Peoples R China 5.East China Normal Univ, Acad Stat & Interdisciplinary Sci, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 | ZHENG, Siming,ZHANG, Juan,ZHOU, Yong. Likelihood identifiability and parameter estimation with nonignorable missing data[J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,2022:20. |
| APA | ZHENG, Siming,ZHANG, Juan,&ZHOU, Yong.(2022).Likelihood identifiability and parameter estimation with nonignorable missing data.CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,20. |
| MLA | ZHENG, Siming,et al."Likelihood identifiability and parameter estimation with nonignorable missing data".CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE (2022):20. |
入库方式： OAI收割
来源：数学与系统科学研究院
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