Abstract
General sufficient conditions for the moderate deviations of M-estimators are presented. These results are applied to many different types of M-estimators such as thep-th quantile, the spatial median, the least absolute deviation estimator in linear regression, maximum likelihood estimators and other location estimators. Moderate deviations theorems from empirical processes are applied.
Key Words
M-estimators moderate deviations maximum likelihood estimatorsAMS subject classification
60F10 62E20Preview
Unable to display preview. Download preview PDF.
References
- Arcones, M. A. (1998). Weak convergence of convex stochastic processes.Statistics and Probability Letters, 37:171–182.MATHCrossRefMathSciNetGoogle Scholar
- Arcones, M. A. (2001). The large deviation principle of empirical processes. Technical report, Department of Mathematical Sciences, Binghamton University, Binghamton, NY.Google Scholar
- Arcones, M. A. (20020. The large deviation principle of stochastic processes.Theory of Probability and Its Applications. To appear.Google Scholar
- Deuschel, J. D. andStroock, D. W. (1989).Large Deviations, Academic Press, Inc., Boston, MA.MATHGoogle Scholar
- Dudley, R. M. (1999).Uniform Central Limit Theorems. Cambridge University Press, Cambridge.MATHGoogle Scholar
- Gao, F. (2001). Moderate deviations for the maximum likelihood estimator.Statistics and Probability Letters, 55:345–352.MATHCrossRefMathSciNetGoogle Scholar
- Giné, E. andZinn, J. (1984). Some limit theorems for empirical processes.Annals of Probability 12:929–989.MATHMathSciNetGoogle Scholar
- Haldane, S. J. (1948). Note on the median of a multivariate distribution.Biometrika, 35:414–415.MATHMathSciNetGoogle Scholar
- Hampel, F. R. (1974). The influence curve and its role in robust estimation.Journal of the American Statistical Association, 69:383–393.MATHCrossRefMathSciNetGoogle Scholar
- Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., andStahel, W. A. (1986)Robust Statistics, the Approach Based on Influence Functions. Wiley, New York.MATHGoogle Scholar
- Huber, P. J. (1964). Robust estimation of a location parameter.Annals of Mathematical Statistics, 35:73–101.MathSciNetGoogle Scholar
- Huber, P. J. (1981).Robust Statistics. Wiley, New York.MATHCrossRefGoogle Scholar
- Jensen, J. L. andWood, A. T. A. (1998). Large deviation and other results for minimum contrast estimators.Annals of the Institute of Statistical Mathematics, 50:673–695.MATHCrossRefMathSciNetGoogle Scholar
- Ledoux, M. andTalagrand, M., (1991).Probability in Banach Spaces. Springer-Verlag, New York.MATHGoogle Scholar
- Lehmann, E. L. andCasella, G. (1998).Theory of Point Estimation. Springer Texts in Statistics,. Springer-Verlag, New York, 2nd ed.MATHGoogle Scholar
- Mäkeläinen, T., Schmidt, K., andStyan, G. P. H. (1981). On the existence and uniqueness of the maximum likelihood estimate of a vectorvalued parameter in fixed-size samples.Annals of Statistics, 9:758–767.MATHMathSciNetGoogle Scholar
- Milasevic, P. andDucharme, G. R. (1987). Uniqueness of the spatial median.Annals of Statistics, 15:1332–1333.MATHMathSciNetGoogle Scholar
- Ossiander, M. (1987). A central limit theorem under entropy withl 2 bracketing.Annals of Probability, 15:897–919.MATHMathSciNetGoogle Scholar
- Portnoy, S. andKoenker, R. (1997). The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators.Statistical Science, 12:279–300.MATHCrossRefMathSciNetGoogle Scholar
- Prokhorov, Y. V. (1959). An extremal problem in probability theory.Theory of Probability and its Applications, 4:211–214.CrossRefGoogle Scholar
- Serfling, R. J. (1980).Approximation Theorems of Mathematical Statistics, Wiley, New York.MATHGoogle Scholar
Copyright information
© Sociedad Española de Estadistica e Investigacion Operativa 2002