Maximum likelihood principle and model selection when the true model is unspecified

Journal of Multivariate Analysis

Volumn 27, Issue 2, 1988, Pages 392-403

  Nishii, R ^a,b  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b HIROSHIMA UNIVERSITY   (Japan)

Author keywords

AIC; BIC; consistency; law of iterated logarithm; MLE; regularity conditions

Indexed keywords

EID: 0001662579     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/0047-259X(88)90137-6     Document Type: Article

References (18)

1. Information theory and an extension of the maximum likelihood principle
2. Limiting behavior of posterior distributions when the model is incorrect
3. Consistency a posteriori
4. Tests of separate families of hypotheses
5. Further results on tests of separate families of hypothesis
6. The performance of the likelihood ratio test when the model is incorrect
7. The determination of the order of an autoregression
8. The behavior of maximum likelihood estimates under non-standard conditions
9. The underlying structure of nonnested hypothesis tests
10. On some asymptotic properties of maximum likelihood estimates and related Bayes estimates
11. Modeling by shortest data description
12. A multiple divergence criterion for testing between separate hypotheses
13. Estimating the dimension of a model
14. Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
15. Note on the consistency of the maximum likelihood estimate
16. Maximum likelihood estimation of misspecified models
17. On detection of the number of signals in presence of white noise