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Bias Adjustment Minimizing the Asymptotic Mean Square Error

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  • Haruhiko Ogasawara

Abstract

A method of bias adjustment which minimizes the asymptotic mean square error is presented for an estimator typically given by maximum likelihood. Generally, this adjustment includes unknown population values. However, in some examples, the adjustment can be done without population values. In the case of a logit, a reasonable fixed value for the adjustment is found, which gives the asymptotic mean square error smaller than those of the asymptotically unbiased estimator and the maximum likelihood estimator. The weighted-score method, which yields directly the estimator with the minimized asymptotic mean square error, is also given.

Suggested Citation

  • Haruhiko Ogasawara, 2015. "Bias Adjustment Minimizing the Asymptotic Mean Square Error," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(16), pages 3503-3522, August.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:16:p:3503-3522
    DOI: 10.1080/03610926.2013.786788
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    Cited by:

    1. Ogasawara, Haruhiko, 2016. "Asymptotic expansions for the estimators of Lagrange multipliers and associated parameters by the maximum likelihood and weighted score methods," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 20-37.

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