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Abstract

For regular parametric models, estimators converge uniformly at a rate n −1/2, and the limit distribution is normal with mean 0. The situation is different if the best possible rate is n −α, with α∈ (0, 1/2), as common for nonparametric models. In this case, uniformly attainable normal limit distributions with mean 0 are impossible.

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REFERENCES

  • Akahira, M. and Takeuchi, K. (1981). Asymptotic Efficiency of Statistical Estimators, Concepts and Higher Order Asymptotic Efficiency, Lecture Notes in Statist., 7, Springer, New York.

    Google Scholar 

  • Akahira, M. and Takeuchi, K. (1995). Non-Regular Statistical Estimation, Lecture Notes in Statist., 107, Springer, New York.

    Google Scholar 

  • Beran, R. (1974). Asymptotically efficient adaptive rank estimates in location models, Ann. Statist., 2, 63–74.

    Google Scholar 

  • Bickel, P. and Hodges, J. L. (1967). The asymptotic theory of Galton's test and a related simple estimate of location, Ann. Math. Statist., 38, 73–89.

    Google Scholar 

  • Bickel, P. J., Klaassen, Ch. A., Ritov, Y. and Wellner, J. A. (1993). Efficient and Adaptive Estimation for Semiparametric Models, John Hopkins, Baltimore and London.

    Google Scholar 

  • Birgé, L. (1987). Estimating a density under order restrictions: Nonasymptotic minimax risk, Ann. Statist., 15, 995–1012.

    Google Scholar 

  • Chow, Y. S. and Teicher, H. (1978). Probability Theory. Independence, Interchangeability, Martigales, Springer, New York.

    Google Scholar 

  • Csörgö, S., Deheuvels, P. and Mason, P. (1985) Kernel estimates of the tail index of a distribution, Ann. Statist., 13, 1050–1077.

    Google Scholar 

  • Donoho, D. L. and Liu, R. C. (1991). Geometrizing rates of convergence, II, Ann. Statist., 19, 633–667.

    Google Scholar 

  • Drees, H. (1998). Optimal rates of convergence for estimates of the extreme value index, Ann. Statist., 26, 434–448.

    Google Scholar 

  • Eddy, W. F. (1980). Optimum kernel estimators of the mode, Ann. Statist., 8, 870–882.

    Google Scholar 

  • Farrell, W. (1972). On the best obtainable asymptotic rates of convergence in the estimation of a density function at a point, Ann. Math. Statist., 43, 170–180.

    Google Scholar 

  • Golubev, G. K. and Levit, B. Y. (1996). Asymptotically efficient estimation for analytic distributions, Math. Methods Statist., 5, 357–368.

    Google Scholar 

  • Golubev, G. K. and Levit, B. Y. (1998). Asymptotically efficient estimation in the Wicksell problem, Ann. Statist., 26, 2407–2419.

    Google Scholar 

  • Groeneboom, P. (1987). Asymptotics for incomplete censored observations, Report 87-18, Fakulteit Wiskunde & Informatia, University of Amsterdam.

  • Groeneboom, P. (1989). Brownian motion with a parabolic drift and Airy functions, Probab. Theory Related Fields, 81, 79–109.

    Google Scholar 

  • Groeneboom, P. and Jongbloed, G. (1995). Isotonic estimation and rates of convergence in Wicksell's problem, Ann. Statist., 23, 1518–1542.

    Google Scholar 

  • Groeneboom, P. and Wellner, J. A. (1992). Information Bounds and Nonparametric Maximum Likelihood Estimation, Birkhäuser, Basel.

    Google Scholar 

  • Hall, P. and Welsh, A. M. (1984). Best attainable rates of convergence for estimates of parameters of regular variation, Ann. Statist., 12, 1079–1084.

    Google Scholar 

  • Has'minskii, R. Z. (1979). Lower bound for the risks of nonparametric estimates of the mode, Contributions to Statistics. Jaroslav Hájek Memorial Volume (ed. J. Jureckova), 91–97, Academia, Prague.

    Google Scholar 

  • Ibragimov, I. A. and Has'minskii, R. Z. (1981). Statistical Estimation. Asymptotic Theory, Springer, New York.

    Google Scholar 

  • Kiefer, J. (1982). Optimum rates for non-parametric density and regression estimates, under order restrictions, Statistics and Probability: Essays in Honor of C. R. Rao (eds. G. Kallianpur, P. R. Krishnaiah and J. K. Ghosh), 419–428, North-Holland, Amsterdam.

    Google Scholar 

  • Millar, P. W. (1983). The minimax principle in asymptotic statistical theory, Lecture Notes in Math., 976 (eds. A. Dold and B. Eckmann), 75–265, Springer, New York.

    Google Scholar 

  • Petrov, V. V. (1995). Limit Theorems of Probability Theory. Sequences of Independent Random Variables, Clarendon Press, Oxford.

    Google Scholar 

  • Pfanzagl, J. (with the assistance of W. Wefelmeyer) (1982). Contributions to a General Asymptotic Statistical Theory, Lecture Notes in Statist., 31, Springer, New York.

    Google Scholar 

  • Pfanzagl, J. (1994). Parametric Statistical Theory, Walter de Gruyter, Berlin.

    Google Scholar 

  • Pfanzagl, J. (1998). On local uniformity for estimators and confidence limits, J. Statist. Plann. Inference (to appear).

  • Prakasa Rao, B. L. S. (1969). Estimation of a unimodal density, Sankhya Ser. A, 31, 23–36.

    Google Scholar 

  • Rao, C. R. (1963). Criteria for estimation in large samples, Sankhya Ser. A, 25, 189–206.

    Google Scholar 

  • Rao, P. V., Schuster, E. F. and Littell, R. C. (1975). Estimation of shift and center of symmetry based on Kolmogorov-Smirnov statistics, Ann. Statist., 3, 862–873.

    Google Scholar 

  • Seneta, E. (1976). Regularly Varying Functions, Lecture Notes in Math., 508, Springer, New York.

    Google Scholar 

  • Smith, R. L. (1985). Maximum likelihood estimation in a class of nonregular cases, Biometrika, 72, 67–90.

    Google Scholar 

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Pfanzagl, J. On Rates and Limit Distributions. Annals of the Institute of Statistical Mathematics 51, 755–778 (1999). https://doi.org/10.1023/A:1004043532578

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  • DOI: https://doi.org/10.1023/A:1004043532578

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