Abstract
This paper considers delta estimators of the Radon-Nikodym derivative of a probability function with respect to a σ-finite measure. We provide sufficient conditions for universal consistency, which are checked for some wide classes of nonparametric estimators.
Similar content being viewed by others
References
Abou-Jaoude, S. (1976a). Sur une condition nécessaire et suffisante deL 1 convergence presque complete de l'estimateur de la partition fixe pour une density,Comptes Rendus de l'Academie des Sciences de Paris, Serie A,283, 1107–1110.
Abou-Jaoude, S. (1976b). Sur la convergenceL 1 etL ∞ de l'estimateur de la partition aléatorie pour une densité,Annales de l'Institut Henry Poincairé,12, 299–317.
Abou-Jaoude, S. (1976c). Conditions nécessaries et suffisantes de convergenceL 1 in probabilité de l'histogram pour une densité,Annales de l'Institut Henry Poincairé,12, 213–231.
Araujo, A. and Giné, E. (1980).The Central Limit Theorem for Real and Banach Valued Random Variables, John Wiley & Sons, New York.
Basawa, I. V. and Prakasa Rao, B. L. S. (1980).Statistical Inference for Stochastic Processes, Academic Press, New York.
Billingsley, P. (1986).Probability and Measure, 2nd ed., John Wiley & Sons, New York.
Bosq, D. (1969). Sur l'estimation d'une densité multivariée par une serie de fonctions orthogonales,Comptes Rendus de l'Academie des Sciences de Paris,268, 555–557.
Butzer, P. L. and Nessel, R. J. (1971).Fourier Analysis and Approximation, Vol. 1, Birkhäuser Verlag, Bassel and Stuttgart.
Čencov, N. N. (1962). Evaluation of an unknown density by orthogonal series,Soviet Mathematics Doklady,3, 1559–1562.
Cheney, E. W. (1982).Introduction to Approximation Theory, Chelsea Publishing Company, New York.
Chung, K. L. (1974).A Course in Probability Theory, 2nd ed., Academic Press, San Diego, California.
Davis, P. J. (1975).Interpolation and Approximation, Dover, New York.
de Guzman, M. (1975). Differentiation of Integrals in ℝn, Lecture Notes in Mathematics,481, Springer Verlag, Berlin.
DeVore, R. A. and Lorentz, G. G. (1993).Constructive Approximation.Grundlehren der mathemastischen Wissenschaften,303, Springer Verlag, Berlin.
Devroye, L. (1983). The equivalence of weak, strong and complete convergence inL 1 for kernel density estimates,Annals of Statistics,11, 896–904.
Devroye, L. (1987). A course in density estimation,Progress in Probability and Statistics, Birkhäuser, Boston.
Devroye, L. (1991). Exponential inequalities in nonparametric estimation,Nonparametric Functional Estimation and Related Topics (ed. G. Roussas), 31–44, Kluwer Academic Publishers, Dordrecht.
Devroye, L. and Györfi, L. (1983). Distribution-free exponential bound on theL 1 error of partitioning estimates of a regression function,Proceedings of the Fourth Pannonian Symposium on Mathematical Statistics (eds. F. Konecny, J. Mogyorodi and W. Wertz), Akadémiai Kiadó, Budapest, Hungary.
Devroye, L. and Györfi, L. (1985a).Nonparametric Density Estimation, The L 1 View, John Wiley & Sons, New York.
Devroye, L. and Györfi, L. (1985b). Distribution free exponential bound for theL 1 error of the partitioning estimates of a regression function,Probability and Statistical Decision Theory, Proceedings of the Fourth Pannonian Symposium on Mathematica Statistics (eds. F. Konecny, J. Mogyorodi and W. Wertz), 67–76, Reidel, Dordrecht.
Devroye, L. and Krzyżak, A. (1989). An equivalence theorem forL 1 convergence of nearest neighbor regression function estimates,Annals of Statistics,22, 1371–1385.
Devroye, L. and Lugosi, G. (2001).Combinatorial Methods in Density Estimation, Springer Verlag, New York.
Devroye, L. and Wagner, T. J. (1979). On theL 1 convergence of kernel density estimators,Annals of Statistics,7, 1136–1139.
Devroye, L. and Wagner, T. J. (1980a). Distribution free consistency results in nonparametric discrimination and regression function estimates,Annals of Statistics,8 231–239.
Devroye, L. and Wagner, T. J. (1980b). On theL 1 convergence of kernel estimators of regression functions with application in discrimination,Zeitschrift für Wahrsheinlichkeitstheorie und verwandte Gebiete,51, 15–25.
Devroye, L. and Wagner, T. J. (1982). Nearest neighbor methods in discrimination,Handbook of Statistics (eds. P. Krishnaiah and L. Kanal), Vol. 2, 193–197, North Holland, Amsterdam.
Devroye, L., Györfi, L., Krzyżak, A. and Lugosi, G. (1996a). On the strong universal consistency of nearest neighbor regression fucntion estimates,Annals of Statistics,22, 1371–1385.
Devroye, L., Györfi, L. and Lugosi, G. (1996b). A probabilistic theory of pattern recognition,Applications of Mathematics, Stochastic Modelling and Applied Probability, Springer Verlag, New York.
Dunford, N. and Schwartz, J. T. (1957).Linear Operators. Part I. General Theory, Wiley Classics Library Edition, 1988. John Wiley & Sons, New York.
Edgar, G. A. and Sucheston, L. (1992). Stopping times and directed processes,Encyclopedia of Mathematics and Its Applications (ed. G. C. Rota),47, Cambridge University Press, Cambridge.
Freedman, D. and Diaconis, P. (1981). On the histogram as a density estimator:L 2 theory,Zeitschrift fur Wahrsheinlichkeitstheorie und verwandte Gebiete,58, 139–157.
Graunt, J. (1662).Natural and Political Observations Made upon the Bills of Mortality, Martyn, London.
Györfi, L. (1981). The rate of convergence ofK n —NN regression estimation and classification,IEEE Transactions on Information Theory,IT-27, 500–509.
Györfi, L., Kohler, M., Krzyżak, A. and Walk, H. (2002).A Distribution-free Theory of Nonparametric Regression, Springer Verlag, New York.
Hoffmann-Jørgensen, J. (1974). Sums of independent Banach space valued random variables,Studia Mathematica,52, 159–186.
Hoffmann-Jørgensen, J. (1976).Probability in Banach Spaces, Lecture Notes in Mathematics,598, Springer Verlag, New York.
Hong, D. H., Ordoñez-Cabrera, M., Sung, S. H. and Volodin, A. I. (2000). On the weak law for randomly indexed partial sums for arrays of random elements in martingale typep Banach spaces,Statistics and Probability Letters,46, 177–185.
Hu, T. C., Ordoñez-Cabrera, M. and Volodin, A. I. (2001). Convergence of randomly weighted sums of B-space valued random elements and uniform integrability concerning random weights,Statistics and Probability Letters,51, 155–164.
Hu, T. H. and Chang, H. H. (1997). Complete convergence and the law of large numbers for arrays of random elements,Proceedings, 2nd World Congress of Nonlinear Analysis, Elsevier Science, U.K. (Nonlinear Analysis. Methods and Applications,30, 4257–4266).
Kantorovich, L. V. and Akilov, G. P. (1982).Functional Analysis, 2nd ed., Pergamon Press, Oxford.
Kreyszig, E. (1978).Introductory Functional Analysis with Applications, Wiley Classics Library, John Wiley & Sons, New York.
Ledoux, M. and Talagrand, M. (1991).Probability in Banach Spaces: Isoperimetry and Processes, Springer Verlag, New York.
Linde, W. (1986).Probability in Banach Spaces-Stable and Infinitely Divisible Distributions, John Wiley & Sons, New York.
Lugosi, G. and Nobel, A. (1996). Consistency of data driven histogram methods for density estimation and classification,Annals of Statistics,24, 687–706.
Lugosi, G. and Zeger, K. (1995). Nonparametric estimation via empirical risk minimization,IEEE Transactions on Information Theory,41, 677–678.
McDiarmid, C. (1989). On the method of bounded differences,Surveys in Combinatorics, 148–188, Cambridge University Press, Cambridge.
Ordoñez-Cabrera, M. (1994). Convergence of weighted sums of random variables and uniform integrability concerning the weights,Collectanea Mathematica,45, 121–132.
Patterson, R. F. and Taylor, R. L. (1997). Strong Laws of Large Numbers for negatively dependent random elements,Proceedings, 2nd World Congress of Nonlinear Analysis, Elsevier Science, U.K. (Nonlinear Analysis. Methods and Applications,30, 4229–4235).
Pisier, G. (1986).Probabilistic Methods in the Geometry of Banach Spaces, Lecture Notes in Mathematics,1206, 167–241, Springer, Berlin.
Pisier, G. (1989).The Volume of Convex Bodies and Banach Space Geometry, Cambridge University Press, Cambridge.
Pollard, D. (1984).Convergence of Stochastic Processes, Springer Verlag, New York.
Prakasa Rao, B. L. S. (1983).Nonparametric Functional Estimation, Academic Press, London.
Révesz, P. (1971). Testing of density functions,Periodica Mathematica Hungarica,1, 35–44.
Révesz, P. (1972). On empirical density function,Periodica Mathematica Hungarica,2, 85–110.
Révesz, P. (1973). A strong law of the empirical density function,Transcations of the 6th Prague Conference on Information Theory, 469–472.
Révesz, P. (1974). On empirical density function,Probability and Statistical Methods—Summer School, Bulgarian Academy of Science, Varna, Bulgaria.
Rudin, W. (1966).Real and Complex Analysis, 2nd ed., McGraw Hill, New York.
Schwartz, L. (1981).Geometry and Probability in Banach Spaces, Lecture Notes in Mathematics,852, Springer Verlag, New York.
Scott, D. W. (1979). On optimal data based histograms,Biometrica,66, 605–610.
Scott, D. W. (1992).Multivariate Density Estimation. Theory, Practice, and Visualization, John Wiley & Sons, New York.
Shilov, G. E. and Gurevich, B. L. (1997).Integral, Measure & Derivative: A Unified Approach, Dover, New York.
Silverman, A. N. (1978). Weak and strong uniform consistency of the kernel estimate of a density and its derivatives,Annals of Statistics,6, 177–184.
Stein, E. M. (1970).Singular Integrals and Differentiability Properties of Functions, Princeton University Press, Princeton, New Jersey.
Stone, C. J. (1977). Consistent nonparametric regression,Annals of Statistics,5, 595–645.
Stute, W. (1982). A law of the logarithm for kernel density estimators,Annals of Probability,10, 414–422.
Taylor, R. L. and Hu, T. H. (1987). Strong Laws of Large Numbers for arrays of rowise independents random elements,International Journal of Mathematics and Mathematical Sciences,10, 804–814.
Terrell, G. R. (1984). Efficiency of nonparametric density estimators, Tech. Report, Department of Mathematical Sciences, Rice University, Houston, Texas.
Terrell, G. R. and Scott, D. W. (1992). Variable kernel density estimation,Annals of Statistics,20, 1236–1265.
Tukey, J. W. (1977).Exploratory Data Analysis, Addison-Wesley, Reading, Massachusetts.
Vakhania, N. N. (1981).Probability Distributions on Linear Spaces, North Holland, New York.
Vakhania, N. N., Tarieladze, V. I. and Chobanyan, S. A. (1987). Probability distributions on Banach spaces,Mathematics and Its Applications (Soviet Series), Reldel Publishing Company, Dordrecht (translated from Russian, 1985, Nauka, Moscow).
van der Vaart, A. W. and Wellner, J. A. (1996).Weak Convergence and Empirical Processes with Applications to Statistics, Springer Verlag, New York.
Vapnik, V. (1982).Estimation of Dependencies Based on Empirical Data, Springer Verlag, New York.
Vidal-Sanz, J. M. (1999).Universal Consistency of Delta Estimators: An Approximation Theory Based Approach, Ph.D. Dissertation, Universidad Carlos III de Madrid, Spain (in Spanish).
Walter, G. and Blum, J. R. (1979). Probability density estimation using delta sequences,Annals of Statistics,7, 328–340.
Watson, G. S. and Leadbetter, M. R. (1963). On the estimation of probability density I,Annals of Statistics,34, 480–491.
Watson, G. S. and Leadbetter, M. R. (1964). Hazard analysis II,Shankhyā, series A,26, 101–116.
Wheeden, R. and Zygmund, A. (1977).Measure and Integral, Marcel Dekker, New York.
Whittle, P. (1958). On the smoothing of probability density functions,Journal of the Royal Statistical Society Series B,20, 334–343.
Winter, B. B. (1973). Strong uniform consistency of integrals of density estimation,The Canadian Journal of Statistics,1, 247–253.
Winter, B. B. (1975). Rate of strong consistency of two nonparametric density estimators,Annals of Statistics,3, 759–766.
Woyczyński, W. A. (1978). Geometry and martingales in Banach spaces—Part II: Independent increments,Probability on Banach Spaces (ed. J. Kuebs),Advances in Probability and Related Topics, Vol. 4, 267–519, Marcel Dekker, New York.
Xia, Dao-Xing (1972).Measure and Integration on Infinite-dimensional Spaces: Abstract Harmonic Analysis, Academic Press, New York.
Author information
Authors and Affiliations
About this article
Cite this article
Vidal-Sanz, J.M., Delgado, M.A. Universal consistency of delta estimators. Ann Inst Stat Math 56, 791–818 (2004). https://doi.org/10.1007/BF02506490
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02506490