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Model misspecification effects for biased samples

Author

Listed:
  • George Tzavelas

    (University of Piraeus)

  • Maria Douli

    (University of Piraeus)

  • Polychronis Economou

    (University of Patras)

Abstract

The model misspecification effects on the maximum likelihood estimator are studied when a biased sample is treated as a random one as well as when a random sample is treated as a biased one. The relation between the existence of a consistent estimator under model misspecification and the completeness of the distribution is also considered. The cases of the weight invariant distribution and the scale parameter distribution are examined and finally an example is presented to illustrate the results.

Suggested Citation

  • George Tzavelas & Maria Douli & Polychronis Economou, 2017. "Model misspecification effects for biased samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(2), pages 171-185, February.
    • Handle: RePEc:spr:metrik:v:80:y:2017:i:2:d:10.1007_s00184-016-0597-5
    DOI: 10.1007/s00184-016-0597-5
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    References listed on IDEAS

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    1. Jinchi Lv & Jun S. Liu, 2014. "Model selection principles in misspecified models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 141-167, January.
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    3. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    4. Konstantinos Fokianos & Irene Kaimi, 2006. "On the Effect of Misspecifying the Density Ratio Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 475-497, September.
    5. Lemonte, Artur J., 2013. "On the gradient statistic under model misspecification," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 390-398.
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