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MSE performance of a heterogeneous pre-test estimator

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  • Ohtani, Kazuhiro

Abstract

In this paper, we consider a linear regression model and examine the MSE performance of a heterogeneous pre-test estimator whose components are the adjusted minimum mean-squared error (AMMSE) estimator and the Stein-rule (SR) estimator. It is shown that the heterogeneous pre-test estimator dominates the SR estimator if a critical value of the pre-test is chosen appropriately. By numerical evaluations it is shown that if the number of independent variables (say, k) is 3 and the critical value of the pre-test is chosen appropriately, then the heterogeneous pre-test estimator dominates the positive-part Stein-rule (PSR) estimator. Also, when k = 4 and 5, the MSE performances of the heterogeneous pre-test and PSR estimators are comparable.

Suggested Citation

  • Ohtani, Kazuhiro, 1999. "MSE performance of a heterogeneous pre-test estimator," Statistics & Probability Letters, Elsevier, vol. 41(1), pages 65-71, January.
    • Handle: RePEc:eee:stapro:v:41:y:1999:i:1:p:65-71
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    Cited by:

    1. Alan Wan & Anoop Chaturvedi, 2000. "Operational Variants of the Minimum Mean Squared Error Estimator in Linear Regression Models with Non-Spherical Disturbances," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(2), pages 332-342, June.
    2. Akio Namba & Kazuhiro Ohtani, 2018. "MSE performance of the weighted average estimators consisting of shrinkage estimators," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(5), pages 1204-1214, March.
    3. Maples, Chellie H. & Hagerman, Amy D. & Lambert, Dayton M., 2022. "Ex-ante effects of the 2018 Agricultural Improvement Act’s grassland initiative," Land Use Policy, Elsevier, vol. 116(C).
    4. Reif, Jiri & Vlcek, Karel, 2002. "Optimal pre-test estimators in regression," Journal of Econometrics, Elsevier, vol. 110(1), pages 91-102, September.

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