An expectation conditional maximization algorithm for the skew-normal based stochastic frontier model

被引：0

作者：

Zhu, Xiaonan ^{[1
]}

Wei, Zheng ^{[2
]}

Wang, Tonghui ^{[3
]}

Choy, S. T. Boris ^{[4
]}

Ma, Ziwei ^{[5
]}

机构：

[1] Univ North Alabama, Dept Math, Florence, AL USA

[2] Texas A&M Univ Corpus Christi, Dept Math & Stat, Corpus Christi, TX 78412 USA

[3] New Mexico State Univ, Dept Math Sci, Las Cruces, NM USA

[4] Univ Sydney, Discipline Business Analyt, Sydney, NSW, Australia

[5] Univ Tennessee Chattanooga, Dept Math, Chattanooga, TN USA

来源：

COMPUTATIONAL STATISTICS | 2024年 / 39卷 / 03期

关键词：

ECM algorithm; Maximum likelihood; Stochastic frontier model; Skew-normal distribution; Truncated normal distribution; MAXIMUM-LIKELIHOOD-ESTIMATION; WINSORIZED MEANS; QUADRATIC-FORMS; SPECIFICATION; DISTRIBUTIONS;

D O I：

10.1007/s00180-023-01356-2

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, a feasible expectation-conditional-maximization (ECM) algorithm is developed for finding the maximum likelihood estimates of parameters of the skew-normal based stochastic frontier model. The closed-form formulas for updating parameters in CM-steps are derived. The proposed methodology is illustrated with simulations and a real data example, where we find the new ECM algorithm outperforms the numerical approach adopted in the previous study.

引用

页码：1539 / 1558

页数：20

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