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Composite likelihood methods: Rao-type tests based on composite minimum density power divergence estimator

Author

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  • E. Castilla

    (Complutense University of Madrid)

  • N. Martín

    (Complutense University of Madrid)

  • L. Pardo

    (Complutense University of Madrid)

  • K. Zografos

    (University of Ioannina)

Abstract

This paper is aimed to present a robust extension of the classical Rao test statistic, in the context of composite likelihood ideas and methods. The Rao-type test statistics are defined on the basis of the composite minimum power divergence estimators instead of the composite maximum likelihood estimator. These Rao-type test statistics are used to test simple and composite null hypotheses. Their performance is evaluated in terms of a simulation study which concentrates to the robustness and the comparison of the Rao-type tests with the respective Wald-type tests considered in Castilla et al. (Entropy 20:18, 2018). The proposed here procedures are developed on the basis of the restricted composite minimum density power divergence estimators which are also discussed for the sake of completeness.

Suggested Citation

  • E. Castilla & N. Martín & L. Pardo & K. Zografos, 2021. "Composite likelihood methods: Rao-type tests based on composite minimum density power divergence estimator," Statistical Papers, Springer, vol. 62(2), pages 1003-1041, April.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:2:d:10.1007_s00362-019-01122-x
    DOI: 10.1007/s00362-019-01122-x
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    References listed on IDEAS

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    1. Cristiano Varin & Paolo Vidoni, 2005. "A note on composite likelihood inference and model selection," Biometrika, Biometrika Trust, vol. 92(3), pages 519-528, September.
    2. Gao, Xin & Song, Peter X.-K., 2010. "Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1531-1540.
    3. Abhik Ghosh & Ayanendranath Basu, 2015. "Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(9), pages 2056-2072, September.
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    Cited by:

    1. Castilla, Elena & Zografos, Konstantinos, 2022. "On distance-type Gaussian estimation," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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