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Maximum-likelihood estimation in non-standard conditions
Abstract
The origin of the present paper is the desire to study the asymptotic behaviour of certain tests of significance which can be based on maximum-likelihood estimators. The standard theory of such problems (e.g. Wald(4)) assumes, sometimes tacitly, that the parameter point corresponding to the null hypothesis lies inside an open set in the parameter space. Here we wish to study what happens when the true parameter point, in estimation problems, lies on the boundary of the parameter space.
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Citations
Cited by
Self, S. G., & Liang, K.-Y. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478472 · Cited by 2,661
Maximum likelihood estimation of models for residual covariance in spatial regression
MARDIA, K. V., & MARSHALL, R. J. (1984) · Biometrika · doi:10.1093/biomet/71.1.135 · Cited by 851
The analysis of designed experiments and longitudinal data by using smoothing splines
Verbyla, A. P., Cullis, B. R., Kenward, M. G., & Welham, S. J. (1999) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/1467-9876.00154 · Cited by 570
Variable Selection for Portfolio Choice
AÏT‐SAHALI, Y., & Brandt, M. W. (2001) · The Journal of Finance · doi:10.1111/0022-1082.00369 · Cited by 409
Testing for Overdispersion in Poisson and Binomial Regression Models
Dean, C. B. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10475225 · Cited by 386
Metadata
- Authors
- Moran, P. A. P.
- Venue
- Mathematical Proceedings of the Cambridge Philosophical Society
- Publisher
- Cambridge University Press (CUP)
- Published
- 1971-11
- Volume
- 70
- Issue
- 3
- Pages
- 441-450
- ISSN
- 0305-0041 (ISSN-L)
- 1469-8064
- OpenAlex
- W2053362221
- Cited by
- 169
- References
- 0
- FWCI
- 0.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- Moran, P. A. P. (1971). Maximum-likelihood estimation in non-standard conditions. Mathematical Proceedings of the Cambridge Philosophical Society. https://doi.org/10.1017/s0305004100050088
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
