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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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Efficient imputation methods in case of measurement errors.

Anoop Kumar1, Shashi Bhushan2, Shivam Shukla3

  • 1Department of Statistics, Central University of Haryana, Mahendergarh, 123031, India.

Heliyon
|March 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces novel imputation methods for missing data affected by measurement errors (ME). These new techniques offer improved accuracy compared to existing approaches in statistical analysis.

Keywords:
62D0562D10Difference and ratio estimatorsImputationMeasurement errorsMissing data

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Area of Science:

  • Statistics
  • Data Science
  • Survey Methodology

Background:

  • Missing data is a common challenge in statistical analysis.
  • Measurement errors (ME) further complicate the handling of missing observations.
  • Existing imputation methods may not adequately address data with ME.

Purpose of the Study:

  • To develop efficient difference and ratio imputation methods for handling missing observations with measurement errors.
  • To analyze the performance of these new imputation techniques.
  • To compare the proposed methods against existing imputation strategies.

Main Methods:

  • Development of novel difference and ratio imputation techniques.
  • Application of Taylor series expansion for approximating mean square errors.
  • Comparative analysis with state-of-the-art imputation methods.
  • Empirical evaluation using real and simulated datasets.

Main Results:

  • The proposed imputation methods demonstrate efficiency in handling missing data with ME.
  • Theoretical analysis using Taylor series expansion provides insights into performance.
  • Empirical studies confirm the practical utility and improved accuracy of the developed imputations.
  • Comparison indicates advantages over existing imputation techniques.

Conclusions:

  • The developed imputation methods are effective for datasets with missing values and measurement errors.
  • These methods offer a valuable improvement for statistical analysis in practical applications.
  • The study provides guidance for sampling practitioners on utilizing these advanced imputation techniques.