Imputation of Missing Data Using Ensemble Algorithms

摘要：

Missing data or incomplete data are very common in statistical situations. One way to deal with missing data is to conduct model imputation either one time or multiple times. One of the key problems in analyzing the imputed dataset is to give the valid statistical reference of the parameter estimated, that is, to give a right estimation of the standard error of the interested statistic. This paper proposes the new developed ensemble algorithms as imputation model. In order to realize multiple imputation, we suggest bootstrap sampling the prediction error several times. The properties of the proposed methods are studied by simulation and compared with existing methods. Finally, the methods are applied to analyze one real large dataset, taking the missing mechanism into consideration.

关键词： ensemble algorithm imputation missing data

作者: Xiaoling Lu Jiesheng Si Lanfeng Pan Yanyun Zhao

作者单位: Center for Applied Statistics, Renmin University of China, Beijing, China School of Statistics, Renm School of Statistics, Renmin University of China, Beijing, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1365-1368

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）