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

Author

Listed:
  • Xiaonan Zhu

    (University of North Alabama)

  • Zheng Wei

    (Texas A &M University - Corpus Christi)

  • Tonghui Wang

    (New Mexico State University)

  • S. T. Boris Choy

    (The University of Sydney)

  • Ziwei Ma

    (University of Tennessee at Chattanooga)

Abstract

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.

Suggested Citation

  • Xiaonan Zhu & Zheng Wei & Tonghui Wang & S. T. Boris Choy & Ziwei Ma, 2024. "An expectation conditional maximization algorithm for the skew-normal based stochastic frontier model," Computational Statistics, Springer, vol. 39(3), pages 1539-1558, May.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01356-2
    DOI: 10.1007/s00180-023-01356-2
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