Modified maximum likelihood estimator under the Jones and Faddy's skew t-error distribution for censored regression model

Sukru Acitas, Ismail Yenilmez, Birdal Senoglu and Yeliz Mert Kantar

Journal of Applied Statistics, 2021, vol. 48, issue 12, 2136-2151

Abstract: It is well-known that classical Tobit estimator of the parameters of the censored regression (CR) model is inefficient in case of non-normal error terms. In this paper, we propose to use the modified maximum likelihood (MML) estimator under the Jones and Faddy's skew t-error distribution, which covers a wide range of skew and symmetric distributions, for the CR model. The MML estimators, providing an alternative to the Tobit estimator, are explicitly expressed and they are asymptotically equivalent to the maximum likelihood estimator. A simulation study is conducted to compare the efficiencies of the MML estimators with the classical estimators such as the ordinary least squares, Tobit, censored least absolute deviations and symmetrically trimmed least squares estimators. The results of the simulation study show that the MML estimators work well among the others with respect to the root mean square error criterion for the CR model. A real life example is also provided to show the suitability of the MML methodology.

Date: 2021
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DOI: 10.1080/02664763.2020.1786673

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