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A new general biased estimator in linear measurement error model

Author

Listed:
  • Pragya Goyal
  • Manoj K. Tiwari
  • Vikas Bist
  • Faisal Ababneh

Abstract

Numerous biased estimators are known to circumvent the multicollinearity problem in linear measurement error models. This article proposes a general biased estimator with the ridge regression and the Liu estimators as special cases. The efficiency of the suggested estimator is compared with ridge regression and Liu estimators under the mean squared error matrix criterion. In addition, a Monte Carlo simulation study and a numerical evaluation have been conducted to elucidate the superiority of the new general biased estimator over other estimators.

Suggested Citation

  • Pragya Goyal & Manoj K. Tiwari & Vikas Bist & Faisal Ababneh, 2025. "A new general biased estimator in linear measurement error model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(10), pages 2827-2843, May.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:10:p:2827-2843
    DOI: 10.1080/03610926.2024.2376667
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