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A Robust Generalization of the Rao Test

Author

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  • Ayanendranath Basu
  • Abhik Ghosh
  • Nirian Martin
  • Leandro Pardo

Abstract

This article presents new families of Rao-type test statistics based on the minimum density power divergence estimators which provide robust generalizations for testing simple and composite null hypotheses. The asymptotic null distributions of the proposed tests are obtained and their robustness properties are also theoretically studied. Numerical illustrations are provided to substantiate the theory developed. On the whole, the proposed tests are seen to be excellent alternatives to the classical Rao test as well as other well-known tests.

Suggested Citation

  • Ayanendranath Basu & Abhik Ghosh & Nirian Martin & Leandro Pardo, 2022. "A Robust Generalization of the Rao Test," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 868-879, April.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:868-879
    DOI: 10.1080/07350015.2021.1876711
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    Cited by:

    1. Ángel Felipe & María Jaenada & Pedro Miranda & Leandro Pardo, 2023. "Restricted Distance-Type Gaussian Estimators Based on Density Power Divergence and Their Applications in Hypothesis Testing," Mathematics, MDPI, vol. 11(6), pages 1-41, March.

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