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Hypothesis testing for two discrete populations based on the Hellinger distance

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  • Basu, A.
  • Mandal, A.
  • Pardo, L.

Abstract

Our interest is in the problem where independent samples are drawn from two different discrete populations, possibly with a common parameter. The goal is to test hypothesis about the parameters involved in these two samples. A number of tests are developed for the above purpose based on the Hellinger distance and penalized versions of it. The asymptotic distributions of the test statistics are derived. Extensive simulation results are provided, which illustrate the theory developed and the robustness of the methods.

Suggested Citation

  • Basu, A. & Mandal, A. & Pardo, L., 2010. "Hypothesis testing for two discrete populations based on the Hellinger distance," Statistics & Probability Letters, Elsevier, vol. 80(3-4), pages 206-214, February.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:3-4:p:206-214
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    References listed on IDEAS

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    1. Basu, Ayanendranath & Harris, Ian R. & Basu, Srabashi, 1996. "Tests of hypotheses in discrete models based on the penalized Hellinger distance," Statistics & Probability Letters, Elsevier, vol. 27(4), pages 367-373, May.
    2. Salicru, M. & Morales, D. & Menendez, M. L. & Pardo, L., 1994. "On the Applications of Divergence Type Measures in Testing Statistical Hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 372-391, November.
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    1. Mandal, Abhijit & Basu, Ayanendranath, 2013. "Minimum disparity estimation: Improved efficiency through inlier modification," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 71-86.
    2. Paul, Subhadeep & Basu, Ayanendranath, 2015. "On second order efficient robust inference," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 187-207.

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