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Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling.

Anoop Kumar1, Shashi Bhushan2, Walid Emam3

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

Scientific Reports
|August 4, 2024
PubMed
Summary
This summary is machine-generated.

Ranked set sampling (RSS) improves estimation efficiency. This study introduces new logarithmic imputation methods to handle missing data in RSS, outperforming existing techniques in simulations and real-world applications.

Keywords:
ImputationMissing dataRanked set samplingSimulation study

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Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Ranked set sampling (RSS) enhances estimator efficiency over simple random sampling.
  • Missing data presents challenges in statistical estimation, with limited research on addressing it within RSS frameworks.

Purpose of the Study:

  • To propose novel logarithmic imputation methods for estimating population mean under RSS.
  • To utilize auxiliary information for improved imputation accuracy.
  • To evaluate the performance of these new methods.

Main Methods:

  • Development of logarithmic type imputation techniques tailored for RSS.
  • Examination of the statistical properties of the proposed imputation procedures.
  • Comparative analysis through a simulation study and real-world data applications.

Main Results:

  • The proposed logarithmic imputation methods effectively address missing data in RSS.
  • Simulation results demonstrate superior performance compared to existing imputation procedures.
  • Real-world applications confirm the generalizability and effectiveness of the new methods.

Conclusions:

  • The novel logarithmic imputation methods offer a significant advancement for handling missing data in ranked set sampling.
  • These methods provide more efficient and accurate estimation of population means when auxiliary information is available.