数据缺失机制识别联合模型及评价

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英文篇名：Combined Model of Data Missing Mechanism Recognition and Its Evaluation 作者：岳廷妍 ; 邱建青 ; 裴姣 ; 张韬 英文作者：Yue Tingyan;Qiu Jianqing;Pei Jiao;Zhang Tao;West China School of Public Health/West China Fourth Hospital,Sichuan University;Institute of Sichuan Cancer Hospital/Sichuan Cancer Prevention & Control Center;School of Medicine,University of Electronic Science and Technology of China; 关键词：缺失机制 ; 缺失比例 ; 联合模型 ; Little ; MCAR法 ; Logit响应模型法 英文关键词：missing mechanism;;missing proportions;;combined model;;Little MCAR method;;Logit response model method 中文刊名：统计与决策 英文刊名：Statistics & Decision 机构：四川大学华西公共卫生学院/四川大学华西第四医院;四川省肿瘤医院研究所/四川省癌症防治中心;电子科技大学医学院; 出版日期：2019-08-09 17:04 出版单位：统计与决策 年：2019 期：16 基金：国家自然科学基金青年项目（81602935）;; 四川大学青年教师科研启动基金（2016SCU11006） 语种：中文; 页：73-76 页数：4 CN：42-1009/C ISSN：1002-6487 分类号：O212

摘要

文章提出了数据缺失机制识别联合模型,并运用R 3.4.1软件、采用Bootstrap法重复模拟对所提出的联合模型在不同缺失机制、不同缺失比例下的识别效果进行评价。从重复模拟结果可知,联合模型在不同缺失比例下对完全随机缺失(MCAR)机制的识别效果较好(正确识别率为94.79%～95.29%),对随机缺失(MAR)机制的识别效果尚可(正确识别率为77.64%～78.72%)。联合模型在两种缺失机制下在各缺失比例下的正确识别率均较为稳健。
        This paper proposes a data missing mechanism recognition combined model, and uses R 3.4.1 software and Bootstrap method to duplicate the simulation to evaluate the recognition effect of the combined model under different missing mechanisms and different missing proportions. The repeated simulation results show that under different missing proportion, the combined model has better recognition effect on the missing completely at random(MCAR) mechanism(correct recognition rate:94.79%～95.29%), and the recognition effect for the missing at random(MAR) mechanism is also acceptable(correct recognition rate:77.64%～78.72%). In the two missing mechanisms, the combined model is robust to the missing proportion.

引文

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