Wavelet-based Estimator for Derivatives of Density Function for Censored and Extended Negatively Dependent Observations

Document Type : Scientific Research

Author

Abstract

Wavelet Analysis is a branch of Harmonic Analysis and a new phenomenon of Mathematics science which offers wide range of application in Mathematics, Statistics and other fields. Wavelets analysis is finding a rapidly growing number of applications despite its young age and often replacing the conventional Fourier transform. Basically in this paper, the problem of estimating a density and its derivatives for a sample of censored random variables is considered. The purpose of this paper is to present an approach to this problem based on wavelets methods for extended negatively dependent observations. Besides, we explore its performances under the  risk in Besov ball.

Keywords


[1] Abbaszadeh, M., Chesneau, C. and Doosti, H. (2012), Nonparametric estimation of a density under bias and multiplicative censoring via wavelet methods, Statistics and Probability Letters, 82, 932-941.
[2] Antoinadis, A. and R. Carmona, Multiresolution analysis and wavelets for density estimation. Technical report, University of California, Irvine, 1991.
[3] Chen, Y. Chen, A. and Kai W. Ng, (2010). The strong law of large numbers for extended negatively dependent random variables, Journal of Applied Probability, Volume 47, Number 4 (2010), 908-922.
[4] Chesneau, C. and Hosseinioun, N. (2013), On the Wavelet Estimation of a Function in a Density Model with Non-identically Distributed Observations, Chilean Journal of Statistics, Vol. 3, No.1, 31-42.
[5] Chesneau, C. and Doosti, H. (2012), Wavelet linear density estimation for a GARCH model under various dependence structures, Journal of Iranian Statistical Society, 12. 1-21.
[6] Cossette, H.; Marceau, E.; Marri, F. On the compound Poisson risk model with dependence based on a generalized Farlie-Gumbel-Morgenstern copula. Insurance Math.
Econom. 43 (2008), no. 3, 444.455.
[7] Daubechies. I. (1992).Ten lectures on wavelets, CBMS-NSF regional conferences series in applies mathematics. SIAM, Philadelphia
[8] Daubechies .I. (1988). Orthogonal bases of compactly supported wavelets, Communication in pure and Applied Mathematics, 41, 909-996.
[9] Donoho, D. L, Johnstone, I. M. Kerkyacharian, G and Picard, D. (1995). Wavelet shrinkage: Asmptopia (with discussion). Journal of Royal statistical society, ser. B 57, (2), 301-370.
[10] Donoho, D. L., Johnstone, I. M. Kerkyacharian, G and Picard, D. (1996). Density estimation by wavelet thresholding .The Annals of statistics, 2, 508-539.
[11] Doukhan, P. and J.R. Loen (1990), Une note sur la déviation quadratique d'estimateurs de densités par projections orthogonales, C.R. Acad Sci. Paris, t310, série 1, 425-430.
[12] Hardle, W. Kerkyacharian, G. Picard, and Tsybabov, A. (1998). Wavelets Approximation and Statistical Applications. Springer-Verlag, New York.
[13] Hosseinioun. N, Doosti, H., and Nirumand. H.A., (2012). Nonparametric Estimation of the Derivatives of a Density by the method of Wavelet for mixing sequences", Statistical Paper, 53 (1), 195-203.
[14] Kerkyacharian,G,and picard , D. (1992). Density estimation in Besov spaces, Statistics and Probability Letters, 13-15, 24.
[15] Leblanc, F. (1996). Wavelet linear density estimator for a discete –time stochastic process:Lp –losses. Statistics and probability letters, 15, 209-213.
[16] Luo X., Tsai W.-Y., Xu Q.(2009). Pseudo partial likelihood estimators for Cox regression with missing covariates. Biometrika. 2009; 96.
[17] Meyer, Y. (1990). Ondelettes et Operateurs, Herman, paris.
[18] Mallat, S. (1989). A Theory for Multiresolution Signal Decomposition the Wavelet Representation, IEEE Trans. Pattern Anal. And Machine Intelligence, 31, 679-693.
[19] Prakasa Rao, B. L. S. (1983). Nonparametric Functional Estimation, Academic Press, Orlando.
[20] Prakasa Rao, B. L. S. (2003), wavelet linear density estimation for associated sequences. Journal of the Indian Statistical, Association, 41, 369-379.
[21] Tang, Q. and Vernic, R. The impact on ruin probabilities of the association structure among financial risks. Statistics and probability Letters, 77 (2007), no. 14, 1522.1525.
[22] Tribouley, k. (1995). Density estimation by cross-validation with wavelet method. Statistical Neerlandica, 45, 41, 62.

[23] Tribiel, H. (1992). Theory of Function Space . BrikhaBirkhauser Verlag, Berlin.

[24] Tribouley, k. (1995), Density estimation by cross-validation with wavelet method. Statistical Neerlandica, 45, 41, 62.