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Robust properties of likelihood ratio tests
Abstract
The usual asymptotic chi-squared distribution for the likelihood ratio test statistic is based on the assumptions that the data come from the parametric model under consideration and that the parameter satisfies the null hypothesis. In this paper we examine the distribution of the likelihood ratio statistic when the data do not come from the parametric model, but when the ‘nearest’ member of the parametric family still satisfies the null hypothesis. In general, the likelihood ratio statistic no longer follows an asymptotic chi-squared distribution, and an alternative statistic based on the union-intersection approach is proposed.
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EFRON, B., & HINKLEY, D. V. (1978) · Biometrika · doi:10.1093/biomet/65.3.457 · Cited by 952
Cited by
Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
Vuong, Q. H. (1989) · Econometrica · doi:10.2307/1912557 · Cited by 5,929
Posada, D., & Buckley, T. R. (2004) · Systematic Biology · doi:10.1080/10635150490522304 · Cited by 3,927
The Robust Inference for the Cox Proportional Hazards Model
Lin, D. Y., & Wei, L. J. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478874 · Cited by 2,695
On composite marginal likelihoods
Varin, C. (2008) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-008-0060-7 · Cited by 326
A Comparison of Some Error Estimates for Neural Network Models
Tibshirani, R. (1996) · Neural Computation · doi:10.1162/neco.1996.8.1.152 · Cited by 285
Metadata
- Authors
- KENT, J. T.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1982
- Volume
- 69
- Issue
- 1
- Pages
- 19-27
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2081491959
- Cited by
- 252
- References
- 1
- FWCI
- 2.37
- Type
- journal article
- Language
- en
- Open access
- closed
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