A nonparametric assessment of model adequacy based on Kullback-Leibler divergence-Reference-Cited by-同舟云学术

A nonparametric assessment of model adequacy based on Kullback-Leibler divergence

Published:2011-11-19 Issue:2 Volume:23 Page:149-162
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Hsieh Ping-Hung

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s11222-011-9298-0.pdf

Reference31 articles.

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3. Berger, J.O., Guglielmi, A.: Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives. J. Am. Stat. Assoc. 96, 174–184 (2001)

4. Carota, C., Parmigiani, G.: On Bayes factors for nonparametric alternatives. In: Bernardo, J.M., Berger, J.O., David, A.P., Smith, A.F.M. (eds.) Bayesian Statistics 5, pp. 507–511. Clarendon Press, Oxford (1996)

5. Chaganty, N.R., Karandikar, R.L.: Some properties of the Kullback-Leibler number. Sankhyā, Ser. A 58, 69–80 (1996)

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