Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes

Year 2019, , 605 - 609, 01.08.2019

Nesrin Alkan , B. Baris Alkan

https://doi.org/10.16984/saufenbilder.507450

Abstract

In many studies, missing data are the
real trouble to researchers. Because the statistical methods are designed for
complete data sets. Multiple imputation method is developed to solve the
missing data problem. The method is also used effectively in some useful
properties of the Bayes method. If there are missing values in the data set,
Bayesian method can be used to prevent the loss of information. In this study,
the performance of the multiple imputation method is evaluated by generating
survival data with different missing rates and different sample sizes. Also,
informative priors and multiple imputation method are used together to prevent
the missing information in the variable with missing value.

Keywords

Missing Value, Multiple Imputation, Bayesian Cox Regression

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