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2018•International journal of statistics and applicationsOpen access

Comparison of Shrinkage–Based Estimators in the Presence of Missing Data: A Multiple Imputation Analysis

M. T. Nwakuya, JC Nwabueze

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Abstract

In this paper we examined the performance of the mean square error of the Ordinary Least Square (OLS) estimator, Minimum Mean Square Error (MMSE) estimator, N/N shrinkage Estimator (N/NSE) and a proposed Adjusted Minimum Mean Square Error (PAMMSE) estimator in a multiple imputation analysis when data points are missing in different data sets. The program for the proposed adjusted minimum mean square error was written and implemented in R. It is shown by numerical computations that the PAMMSE Estimator seem to be the best choice among OLS, MMSE, N/NSE and PAMMSE estimators in terms of their mean square errors when applied in multiple imputation analysis.

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What this paper is about

In this paper we examined the performance of the mean square error of the Ordinary Least Square (OLS) estimator, Minimum Mean Square Error (MMSE) estimator, N/N shrinkage Estimator (N/NSE) and a proposed Adjusted Minimum Mean Square Error (PAMMSE) estimator in a multiple imputation analysis when data points are missing in different data sets. The program for the proposed adjusted minimum mean square error was written and implemented in R. It is shown by numerical computations that the PAMMSE Estimator seem to be the best choice among OLS, MMSE, N/NSE and PAMMSE estimators in terms of their mean square errors when applied in multiple imputation analysis.

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Available abstract

In this paper we examined the performance of the mean square error of the Ordinary Least Square (OLS) estimator, Minimum Mean Square Error (MMSE) estimator, N/N shrinkage Estimator (N/NSE) and a proposed Adjusted Minimum Mean Square Error (PAMMSE) estimator in a multiple imputation analysis when data points are missing in different data sets. The program for the proposed adjusted minimum mean square error was written and implemented in R. It is shown by numerical computations that the PAMMSE Estimator seem to be the best choice among OLS, MMSE, N/NSE and PAMMSE estimators in terms of their mean square errors when applied in multiple imputation analysis.

Key concepts: Estimator, Minimum mean square error, Mean squared error, Statistics, Mathematics, Missing data, Imputation (statistics), Shrinkage estimator

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