{"title":"缺失数据IPW估算方程","authors":"Hao Wu, Cuicui Li, Chen Cheng","doi":"10.1504/IJRIS.2021.113032","DOIUrl":null,"url":null,"abstract":"The inverse probability weighted (IPW) imputation method is first applied to compensate for non-response. And then, the empirical likelihood (EL) inference is made for estimation equation parameters. It is a nice result to be obtained in this paper that the limiting distributions of the EL statistics are χ2-type distributions under the IPW imputation. Compared with the usually-used methods, the proposed method is easier to complement and performs more efficient.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating equations under IPW imputation of missing data\",\"authors\":\"Hao Wu, Cuicui Li, Chen Cheng\",\"doi\":\"10.1504/IJRIS.2021.113032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The inverse probability weighted (IPW) imputation method is first applied to compensate for non-response. And then, the empirical likelihood (EL) inference is made for estimation equation parameters. It is a nice result to be obtained in this paper that the limiting distributions of the EL statistics are χ2-type distributions under the IPW imputation. Compared with the usually-used methods, the proposed method is easier to complement and performs more efficient.\",\"PeriodicalId\":360794,\"journal\":{\"name\":\"Int. J. Reason. based Intell. Syst.\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Reason. based Intell. Syst.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJRIS.2021.113032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Reason. based Intell. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJRIS.2021.113032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating equations under IPW imputation of missing data
The inverse probability weighted (IPW) imputation method is first applied to compensate for non-response. And then, the empirical likelihood (EL) inference is made for estimation equation parameters. It is a nice result to be obtained in this paper that the limiting distributions of the EL statistics are χ2-type distributions under the IPW imputation. Compared with the usually-used methods, the proposed method is easier to complement and performs more efficient.