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Dual-type quantile regression approach for mean estimation incorporating sampled and non-sampled data.

Abdullah Mohammed Alomair1, Soofia Iftikhar2

  • 1Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

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Summary

This study introduces a new dual-type mean estimator using quantile regression, effective for handling extreme data and sensitive information in surveys. It improves estimation accuracy by incorporating auxiliary variables and addressing nonresponse bias.

Keywords:
Auxiliary informationDual-type estimatorsQuantile regressionSRSSensitive variable

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Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Robust mean estimation is crucial in finite sampling theory.
  • Extreme observations and sensitive data introduce challenges in survey estimation.

Purpose of the Study:

  • Introduce a novel dual-type class of mean estimators.
  • Address challenges posed by extreme observations and sensitive target variables in survey data.

Main Methods:

  • Utilize quantile regression for developing the dual-type mean estimators.
  • Integrate averages of sampled and non-sampled auxiliary variable observations.
  • Explore additive scrambled response methods for sensitive data.

Main Results:

  • The proposed dual-type class demonstrates effectiveness in handling extreme observations.
  • The estimators show improved performance in the presence of nonresponse and inaccurate reporting due to sensitive topics.
  • Numerical studies confirm the class's superiority over existing estimators.

Conclusions:

  • The novel dual-type mean estimators offer a robust approach for finite population sampling.
  • The framework effectively accommodates both non-sensitive and sensitive target variables.
  • The method provides a valuable tool for improving survey data accuracy.