On bias reduction estimators of skew-normal and skew-t distributions

Mohammad Mahdi Maghami, Mohammad Bahrami and Farkhondeh Alsadat Sajadi

Journal of Applied Statistics, 2020, vol. 47, issue 16, 3030-3052

Abstract: A particular concerns of researchers in statistical inference is bias in parameters estimation. Maximum likelihood estimators are often biased and for small sample size, the first order bias of them can be large and so it may influence the efficiency of the estimator. There are different methods for reduction of this bias. In this paper, we proposed a modified maximum likelihood estimator for the shape parameter of two popular skew distributions, namely skew-normal and skew-t, by offering a new method. We show that this estimator has lower asymptotic bias than the maximum likelihood estimator and is more efficient than those based on the existing methods.

Date: 2020
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DOI: 10.1080/02664763.2019.1710114

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