Simulation Study for Missing Data with Multiple Imputation
Date Issued
2005
Date
2005
Author(s)
Huang, Lin-Wei
DOI
zh-TW
Abstract
The purpose of this study is to compare the difference between the method of bootstrap, a data augmentation and multiple imputation for estimating the confidence interval of missing data. In general, there are several methods dealing with missing data, but imputation method is usually used by statisticians. The imputation method can be divided into single imputation and multiple imputation. In early years, single imputation is more convenient. Now lots of software provides the procedure of multiple imputation. The confidence intervals for mean were established by the three methods. Then we show that the multiple imputation is as efficient as bootstrap method.
We simulate data from multivariate normal distribution with three different sample sizes, and set different missing rates it was found that the coverage probability for bootstrap method is approximate to the confidence coefficient. The data augmentation is inferior to both bootstrap method and multiple imputation. The result of the multiple imputation is similar to the bootstrap method. The multiple imputation still estimates the parameters accurately even for high missing rate.
Subjects
遺失資料
EM法
多重插補法
拔靴法
信賴區間
missing data
EM algorithm
multiple imputation
bootstrap
confidence interval
Type
thesis
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