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Bias reduction for the maximum likelihood estimator of the doubly-truncated Poisson distribution

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  • Ryan T. Godwin

Abstract

We derive an analytic expression for the bias of the maximum likelihood estimator of the parameter in a doubly-truncated Poisson distribution, which proves highly effective as a means of bias correction. For smaller sample sizes, our method outperforms the alternative of bias correction via the parametric bootstrap. Bias is of little concern in the positive Poisson distribution, the most common form of truncation in the applied literature. Bias appears to be the most severe in the doubly-truncated Poisson distribution, when the mean of the distribution is close to the right (upper) truncation.

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  • Ryan T. Godwin, 2016. "Bias reduction for the maximum likelihood estimator of the doubly-truncated Poisson distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(7), pages 1887-1901, April.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:7:p:1887-1901
    DOI: 10.1080/03610926.2013.867999
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    Cited by:

    1. Ryan T. Godwin & David E. Giles, 2017. "Analytic Bias Correction for Maximum Likelihood Estimators When the Bias Function is Non-Constant," Econometrics Working Papers 1702, Department of Economics, University of Victoria.
    2. Gómez–Déniz, E. & Pérez–Rodríguez, J.V., 2019. "Modelling distribution of aggregate expenditure on tourism," Economic Modelling, Elsevier, vol. 78(C), pages 293-308.

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