The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Maximum likelihood estimation in a class of nonregular cases
Abstract
We consider maximum likelihood estimation of the parameters of a probability density which is zero for x < θ and asymptotically αc(x-θ)α−1 as x ↓ θ. Here θ and other parameters, which may or may not include α and c1 are unknown. The classical regularity conditions for the asymptotic properties of maximum likelihood estimators are not satisfied but it is shown that, when α> 2, the information matrix is finite and the classical asymptotic properties continue to hold. For α= 2 the maximum likelihood estimators are asymptotically efficient and normally distributed, but with a different rate of convergence. For 1 < α < 2, the maximum likelihood estimators exist in general, but are not asymptotically normal, while the question of asymptotic efficiency is still unsolved. For αα 1, the maximum likelihood estimators may not exist at all, but alternatives are proposed. All these results are already known for the case of a single unknown location parameter θ, but are here extended to the case in which there are additional unknown parameters. The paper concludes with a discussion of the applications in extreme value theory.
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Cites
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,269
Gumbel, E. J. (1958) · Columbia University Press eBooks · doi:10.7312/gumb92958 · Cited by 5,567
Statistical Inference Using Extreme Order Statistics
III, J. P. (1975) · The Annals of Statistics · doi:10.1214/aos/1176343003 · Cited by 3,567
The frequency distribution of the annual maximum (or minimum) values of meteorological elements
Jenkinson, A. F. (1955) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49708134804 · Cited by 1,549
Note on the Consistency of the Maximum Likelihood Estimate
Wald, A. (1949) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729952 · Cited by 1,100
EFRON, B., & HINKLEY, D. V. (1978) · Biometrika · doi:10.1093/biomet/65.3.457 · Cited by 952
Estimating Parameters in Continuous Univariate Distributions with a Shifted Origin
Cheng, R. C. H., & Amin, N. A. K. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01268.x · Cited by 782
Maximum Likelihood Estimation in the Weibull Distribution Based On Complete and On Censored Samples
Cohen, A. C. (1965) · Technometrics · doi:10.1080/00401706.1965.10490300 · Cited by 725
On a formula for the distribution of the maximum likelihood estimator
BARNDORFF-NIELSEN, O. (1983) · Biometrika · doi:10.1093/biomet/70.2.343 · Cited by 466
On the Asymptotic Behaviour of Posterior Distributions
Walker, A. M. (1969) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1969.tb00767.x · Cited by 332
Cited by
Parameter Orthogonality and Approximate Conditional Inference
Cox, D. R., & Reid, N. (1987) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1987.tb01422.x · Cited by 1,366
Statistical Size Distributions in Economics and Actuarial Sciences
Kleiber, C., & Kotz, S. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471457175 · Cited by 1,117
Generalized maximum‐likelihood generalized extreme‐value quantile estimators for hydrologic data
Martins, E. S., & Stedinger, J. R. (2000) · Water Resources Research · doi:10.1029/1999wr900330 · Cited by 687
Asymptotic efficiency of the two-stage estimation method for copula-based models
Joe, H. (2005) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2004.06.003 · Cited by 602
A Generalization of the Weibull Distribution with Application to the Analysis of Survival Data
Mudholkar, G. S., Srivastava, D. K., & Kollia, G. D. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476725 · Cited by 406
Metadata
- Authors
- SMITH, R. L.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1985
- Volume
- 72
- Issue
- 1
- Pages
- 67-90
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2052218905
- Cited by
- 888
- References
- 16
- FWCI
- 10.98
- Type
- journal article
- Language
- en
- Open access
- closed
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