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Abstrakty
Let {Xk, k ≥ 1} be a sequence of independent identically distributed random variables with common probability density function f, and let fn denote a Wegman-Davies recursive density estimator [formula] where K is a kernel function and hn is a band sequence. In the prezent paper, the moderate deviation principle and the large deviation principle for the estimator fn are established.
Czasopismo
Rocznik
Tom
Strony
83--95
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, Henan Province, 453007, China
autor
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, Henan Province, 453007, China
autor
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, Henan Province, 453007, China
autor
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, Henan Province, 453007, China
Bibliografia
- [1] M. Csörgö and L. Horváth, Central limit theorems for Lp-norms of density estimators, Probab. Theory Related Fields 80 (1988), 269-291.
- [2] A. Dembo and O. Zeitouni, Large Deviations Techniques and Applications, Springer, Berlin, 2010.
- [3] F. Q. Gao, Moderate deviations and large deviations for kernel density estimators, J. Theoret. Probab. 16 (2003), 401-418.
- [4] F. Q. Gao, Moderate deviations and law of the iterated logarithm in L1(Rd) for kernel density estimators, Stochastic Process. Appl. 118 (2008), 452-473.
- [5] H. Y. Liang and J. Baek, Asymptotic normality of recursive density estimates under some dependence assumptions, Metrika 60 (2004), 155-166.
- [6] H. Y. Liang and J. Baek, Berry-Esseen bounds for density estimates under NA assumption, Metrika 68 (2008), 305-322.
- [7] D. Louani, Large deviations limit theorems for the kernel density estimator, Scand. J. Statist. 25 (1998), 243-253.
- [8] D. Louani, Large deviations for the L1-distance in kernel density estimation, J. Statist. Plann. Inference 90 (2000), 177-182.
- [9] Z. D. Lu, Asymptotic normality of kernel density estimators under dependence, Ann. Inst. Statist. Math. 53 (2001), 447-468.
- [10] E. Masry, Recursive probability density estimation for weakly dependent stationary processes, IEEE Trans. Inform. Theory 32 (1986), 254-267.
- [11] E. Masry and L. Györfi, Strong consistency and rates for recursive probability density estimators of stationary processes, J. Multivariate Anal. 22 (1987), 79-93.
- [12] A. Mokkadem, M. Pelletier and B. Thiam, Large and moderate deviations principles for recursive kernel estimator of a multivariate density and its partial derivatives, Serdica Math. J. 32 (2006), 323-354.
- [13] A. Mokkadem, M. Pelletier and J. Worms, Large and moderate deviations principles for kernel estimation of a multivariate density and its partial derivatives, Austral. New Zealand J. Statist. 47 (2005), 489-502.
- [14] E. Parzen, On estimation of a probability density function and mode, Ann. Math. Statist. 33 (1962), 1065-1076.
- [15] M. Rosenblatt, Remarks on some nonparametric estimates of a density function, Ann. Math. Statist. 27 (1956), 832-837.
- [16] E. J. Wegman and H. I. Davies, Remarks on some recursive estimators of a probability density, Ann. Statist. 7 (1979), 316-327.
- [17] C. T. Wolverton and T. J. Wagner, Asymptotically optimal discriminant functions for pat tern classification, IEEE Trans. Information Theory 15 (1969), 258-265.
- [18] H. Yamato, Sequential estimation of a continuous probability density function and mode, Bull. Math. Statist. 14 (1971), 1-12.
- [19] D. X. Zhang and H. Y. Liang, Recursive density estimation of NA samples, Chinese J. Appl. Probab. Statist. 24 (2008), 123-134.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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