Bias of the Maximum Likelihood Estimators of the Two-Parameter Gamma Distribution Revisited
We consider the quality of the maximum likelihood estimators for the parameters of the two-parameter gamma distribution in small samples. We show that the methodology suggested by Cox and Snell (1968) can be used very easily to bias-adjust these estimators. A simulation study shows that this analytic correction is frequently much more effective than bias-adjusting using the bootstrap – generally by an order of magnitude in percentage terms. The two bias-correction methods considered result in increased variability in small samples, and the original estimators and their bias-corrected counterparts all have similar percentage mean squared errors.
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- David E. Giles & Hui Feng, 2009.
"Bias - Corrected Maximum Likelihood Estimation of the Parameters of the Generalized Pareto Distribution,"
Econometrics Working Papers
0902, Department of Economics, University of Victoria.
- David E. Giles & Hui Feng & Ryan T. Godwin, 2011. "Bias - Corrected Maximum Likelihood Estimation of the Parameters of the Generalized Pareto Distribution," Econometrics Working Papers 1105, Department of Economics, University of Victoria.
- Cordeiro, Gauss M. & Klein, Ruben, 1994. "Bias correction in ARMA models," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 169-176, February.
- David E. Giles, 2009. "Bias Reduction for the Maximum Likelihood Estimator of the Scale Parameter in the Half-Logistic Distribution," Econometrics Working Papers 0901, Department of Economics, University of Victoria.
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