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On disparity based goodness-of-fit tests for multinomial models

Author

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  • Basu, Ayenendranath
  • Sarkar, Sahadeb

Abstract

A general class of goodness-of-fit tests called disparity tests containing the family of power weighted divergence statistics as a subclass is considered. Under the simple and composite null hypotheses the asymptotic distribution of disparity tests is shown to be chi-square. It is also shown that the blended weight Hellinger distance subfamily, like the power weighted divergence subfamily, has a member that gives an excellent compromise between the Pearson's chi-square and the log likelihood ratio tests.

Suggested Citation

  • Basu, Ayenendranath & Sarkar, Sahadeb, 1994. "On disparity based goodness-of-fit tests for multinomial models," Statistics & Probability Letters, Elsevier, vol. 19(4), pages 307-312, March.
    • Handle: RePEc:eee:stapro:v:19:y:1994:i:4:p:307-312
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    Cited by:

    1. M. Jiménez-Gamero & R. Pino-Mejías & A. Rufián-Lizana, 2014. "Minimum $$K_{\phi }$$ K ϕ -divergence estimators for multinomial models and applications," Computational Statistics, Springer, vol. 29(1), pages 363-401, February.
    2. Park, Chanseok & Basu, Ayanendranath & G. Lindsay, Bruce, 2002. "The residual adjustment function and weighted likelihood: a graphical interpretation of robustness of minimum disparity estimators," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 21-33, March.
    3. Jiménez-Gamero, M.D. & Pino-Mejías, R. & Alba-Fernández, V. & Moreno-Rebollo, J.L., 2011. "Minimum [phi]-divergence estimation in misspecified multinomial models," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3365-3378, December.

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