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Testing with Exponentially Tilted Empirical Likelihood

Author

Listed:
  • A. Felipe

    (Complutense University of Madrid)

  • N. Martín

    (Complutense University of Madrid)

  • P. Miranda

    (Complutense University of Madrid)

  • L. Pardo

    (Complutense University of Madrid)

Abstract

Imposing restrictions without assuming underlying distributions to modelize complex realities is a valuable methodological tool. However, if a subset of restrictions were not correctly specified, the usual test-statistics for correctly specified models tend to reject erronously a simple null hypothesis. In this setting, we may say that the model suffers from misspecification. We study the behavior of empirical phi-divergence test-statistics, introduced in Balakrishnan et al. Statistics 49:951–977 (2015), by using the exponential tilted empirical likelihood estimators of Schennach Ann Stat 35:634–672 (2007), as a good compromise between the efficiency of the significance level for small sample sizes and the robustness under misspecification.

Suggested Citation

  • A. Felipe & N. Martín & P. Miranda & L. Pardo, 2018. "Testing with Exponentially Tilted Empirical Likelihood," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1319-1358, December.
    • Handle: RePEc:spr:metcap:v:20:y:2018:i:4:d:10.1007_s11009-018-9620-9
    DOI: 10.1007/s11009-018-9620-9
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    References listed on IDEAS

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    Cited by:

    1. Amor Keziou & Aida Toma, 2021. "A Robust Version of the Empirical Likelihood Estimator," Mathematics, MDPI, vol. 9(8), pages 1-19, April.

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