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An Exponential-Cum-Sine-Type Hybrid Imputation Technique for Missing Data.

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A novel hybrid imputation technique effectively addresses missing survey data. This method shows promising performance in estimating population means, outperforming existing approaches in simulations.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Data Science

Background:

  • Missing data is a common challenge in surveys, potentially biasing results.
  • Accurate estimation of population parameters is crucial for reliable survey analysis.

Purpose of the Study:

  • To introduce a new exponential-cum-sine-type hybrid imputation technique.
  • To evaluate the bias and mean square errors of the proposed point estimator for population mean.

Main Methods:

  • Developing a novel hybrid imputation technique.
  • Analyzing the statistical properties (bias, MSE) of the estimator.
  • Conducting simulation studies with Normal, Poisson, and Gamma distributions.

Main Results:

  • The proposed imputation technique demonstrated competitive performance.
  • The estimator's properties were evaluated against established methods.
  • Simulation results provide insights into the estimator's effectiveness across different data distributions.

Conclusions:

  • The new hybrid imputation technique offers a viable solution for handling missing survey data.
  • The proposed estimator shows potential for real-life applications in survey analysis.