Performance Comparison of Imputation Methods for Mixed Data Missing at Random with Small and Large Sample Data Set with Different Variability
Kyei Baffour Afari, Christina Nicole Holder Lewis
Asian Journal of Probability and Statistics · pp. 16–39 · Published 1 Oct 2022
10.9734/ajpas/2022/v20i2416Abstract
One of the concerns in the field of statistics is the presence of missing data, which leads to bias in parameter estimation and inaccurate results. However, the multiple imputation procedure is a remedy for handling missing data. This study looked at the best multiple imputation methods used to handle mixed variable datasets with different sample sizes and variability along with different levels of missingness. The study employed the predictive mean matching, classification and regression trees, and the random forest imputation methods. For each dataset, the multiple regression parameter estimates for the complete datasets were compared to the multiple regression parameter estimates found with the imputed dataset. The results showed that the random forest imputation method was the best for mostly a sample of 500 irrespective of the variability. The classification and regression tree imputation methods worked best mostly on sample of 30 irrespective of the variability.
References (22)
- 1 Missing data: Our view of the state of the art. [DOI]
- 2 Applied Missing Data Analysis
- 3 MICE: Multivariate Imputation by Chained Equations in R
- 4 Principled missing data methods for researchers [DOI]
- 5 Statistical Analysis with Missing Data [DOI]
- 6 The Prevention and Treatment of Missing Data in Clinical Trials [DOI]
- 7 The prevention and handling of the missing data [DOI]
- 8 Informative Drop-Out in Longitudinal Data Analysis [DOI]
- 9 Analysis of Longitudinal Data. [DOI]
- 10 Random forest missing data algorithms [DOI]
- 11 Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse [DOI]
- 12 A Review of Methods for Missing Data [DOI]
- 13 Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data [DOI]
- 14 Multiple imputation: review of theory, implementation and software [DOI]
- 15 Markov chain Monte Carlo [DOI]
- 16 A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data [DOI]
- 17 Imputation of Missing Values for Unsupervised Data Using the Proximity in Random Forests
- 18 Imputation of missing values for semi-supervised data using the proximity in random forests [DOI]
- 19 Discriminant Analysis When a Block of Observations is Missing [DOI]
- 20 Performance of Imputation Algorithms on Artificially Produced Missing at Random Data
- 21 Journal of the American Statistical Association [DOI]
- 22 Regression With Missing X's: A Review [DOI]
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