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H-relative error estimation for multiplicative regression model with random effect

Author

Listed:
  • Zhanfeng Wang

    (University of Science and Technology of China)

  • Zhuojian Chen

    (University of Science and Technology of China)

  • Zimu Chen

    (University of Science and Technology of China)

Abstract

Relative error approaches are more of concern compared to absolute error ones such as the least square and least absolute deviation, when it needs scale invariant of output variable, for example with analyzing stock and survival data. A relative error estimation procedure based on the h-likelihood is developed to avoid heavy and intractable integration for a multiplicative regression model with random effect. Statistical properties of the parameters and random effect in the model are studied. Numerical studies including simulation and real examples show the proposed estimation procedure performs well.

Suggested Citation

  • Zhanfeng Wang & Zhuojian Chen & Zimu Chen, 2018. "H-relative error estimation for multiplicative regression model with random effect," Computational Statistics, Springer, vol. 33(2), pages 623-638, June.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-018-0798-7
    DOI: 10.1007/s00180-018-0798-7
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    References listed on IDEAS

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    1. Chen, Kani & Lin, Yuanyuan & Wang, Zhanfeng & Ying, Zhiliang, 2016. "Least product relative error estimation," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 91-98.
    2. Park, Heungsun & Stefanski, L. A., 1998. "Relative-error prediction," Statistics & Probability Letters, Elsevier, vol. 40(3), pages 227-236, October.
    3. Zhanfeng Wang & Wenxin Liu & Yuanyuan Lin, 2015. "A change-point problem in relative error-based regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 835-856, December.
    4. Chen, Kani & Guo, Shaojun & Lin, Yuanyuan & Ying, Zhiliang, 2010. "Least Absolute Relative Error Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1104-1112.
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