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A relative error-based estimation with an increasing number of parameters

Author

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  • Hao Ding
  • Zhanfeng Wang
  • Yaohua Wu

Abstract

The least product relative error (LPRE) estimator and test statistic to test linear hypotheses of regression parameters in the multiplicative regression model are studied when the number of covariate variables increases with the sample size. Some properties of the LPRE estimator and test statistic are obtained such as consistency, Bahadur presentation, and asymptotic distributions. Furthermore, we extend the LPRE to a more general relative error criterion and provide their statistical properties. Numerical studies including simulations and two real examples show that the proposed estimation performs well.

Suggested Citation

  • Hao Ding & Zhanfeng Wang & Yaohua Wu, 2018. "A relative error-based estimation with an increasing number of parameters," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(1), pages 196-209, January.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:1:p:196-209
    DOI: 10.1080/03610926.2017.1301474
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    Cited by:

    1. Huilan Liu & Xiawei Zhang & Huaiqing Hu & Junjie Ma, 2024. "Analysis of the positive response data with the varying coefficient partially nonlinear multiplicative model," Statistical Papers, Springer, vol. 65(5), pages 3063-3092, July.
    2. Yinjun Chen & Hao Ming & Hu Yang, 2024. "Efficient variable selection for high-dimensional multiplicative models: a novel LPRE-based approach," Statistical Papers, Springer, vol. 65(6), pages 3713-3737, August.

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