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Statistische Hefte

, Volume 27, Issue 1, pp 173–178 | Cite as

Finite sample selection criteria for multinomial models

  • H. Linhart
  • W. Zucchini
Articles

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.

Keywords

Model Selection Operating Model Multinomial Model Unbiased Estimator Multinomial Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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.Google Scholar
  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.Google Scholar
  3. Linhart, H., Zucchini, W. (1986). Model selection. J. Wiley, New York.zbMATHGoogle Scholar
  4. Sakamoto, Y, Akaike, H. (1978). Analysis of cross-classified data by AIC. Ann. Inst. Statist. Math., 30, 185–197.zbMATHCrossRefMathSciNetGoogle Scholar
  5. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. J. Roy. Statist. soc. B. 36, 111–133.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag 1986

Authors and Affiliations

  • H. Linhart
    • 1
  • W. Zucchini
    • 2
  1. 1.Institut für Statistik undÖkonometrie der UniversitätGöttingen
  2. 2.Dept. of Math. StatisticsUniversity of Cape TownRondeboschSouth Africa

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