An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm
{"title":"An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm","authors":"Tacksoo Shin, J. Long, M. Davison","doi":"10.1007/s42081-022-00176-w","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"629 - 659"},"PeriodicalIF":1.1000,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese Journal of Statistics and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s42081-022-00176-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
