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Finite sample selection criteria for multinomial models

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Summary

Finite sample selection criteria are derived for the case of multinomial operating and approximating models. The criteria are based on the Kullback-Leibler, the Gauß and the Pearsonchisquared discrepancies and turn out to be crossvalidatory.

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References

  1. Linhart, H., Zucchini, W. (1982). On model selection in analysis of variance. In: B. Fleischmann et al., ed. 's, Oper. Res. Proceedings 1981, 483–493.

  2. Linhart, H., Zucchini, W. (1985). Selection of approximating models by chisquared discrepancies if the operating model is multinomial. Statistics & Dec., Suppl. Issue No. 2, 375–380.

  3. Linhart, H., Zucchini, W. (1986). Model selection. J. Wiley, New York.

  4. Sakamoto, Y, Akaike, H. (1978). Analysis of cross-classified data by AIC. Ann. Inst. Statist. Math., 30, 185–197.

  5. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. J. Roy. Statist. soc. B. 36, 111–133.

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Author information

Correspondence to H. Linhart.

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Linhart, H., Zucchini, W. Finite sample selection criteria for multinomial models. Statistische Hefte 27, 173–178 (1986). https://doi.org/10.1007/BF02932566

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Keywords

  • Model Selection
  • Operating Model
  • Multinomial Model
  • Unbiased Estimator
  • Multinomial Distribution