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Title: Density deconvolution with associated stationary data (English)
Author: Thuy, Le Thi Hong
Author: Phuong, Cao Xuan
Language: English
Journal: Applications of Mathematics
ISSN: 0862-7940 (print)
ISSN: 1572-9109 (online)
Volume: 68
Issue: 5
Year: 2023
Pages: 685-708
Summary lang: English
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Category: math
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Summary: We study the density deconvolution problem when the random variables of interest are an associated strictly stationary sequence and the random noises are i.i.d.\ with a nonstandard density. Based on a nonparametric strategy, we introduce an estimator depending on two parameters. This estimator is shown to be consistent with respect to the mean integrated squared error. Under additional regularity assumptions on the target function as well as on the density of noises, some error estimates are derived. Several numerical simulations are also conducted to illustrate the efficiency of our method. (English)
Keyword: associated process
Keyword: density deconvolution
Keyword: nonstandard noise density
MSC: 62G05
MSC: 62G07
MSC: 62G20
idZBL: Zbl 07790541
idMR: MR4645004
DOI: 10.21136/AM.2023.0135-22
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Date available: 2023-10-05T15:14:01Z
Last updated: 2024-12-13
Stable URL: http://hdl.handle.net/10338.dmlcz/151839
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Reference: [1] Bagai, I., Rao, B. L. S. Prakasa: Kernel-type density and failure rate estimation for associated sequences.Ann. Inst. Stat. Math. 47 (1995), 253-266. Zbl 0833.62036, MR 1345422, 10.1007/BF00773461
Reference: [2] Birkel, T.: On the convergence rate in the central limit theorem for associated processes.Ann. Probab. 16 (1988), 1685-1698. Zbl 0658.60039, MR 0958210, 10.1214/aop/1176991591
Reference: [3] Butucea, C., Tsybakov, A. B.: Sharp optimality in density deconvolution with dominating bias. I.Theory Probab. Appl. 52 (2008), 24-39. Zbl 1141.62021, MR 2354572, 10.1137/S0040585X97982840
Reference: [4] Carrasco, M., Florens, J.-P.: A spectral method for deconvolving a density.Econom. Theory 27 (2011), 546-581. Zbl 1218.62025, MR 2806260, 10.1017/S026646661000040X
Reference: [5] Carroll, R. J., Hall, P.: Optimal rates of convergence for deconvolving a density.J. Am. Stat. Assoc. 83 (1988), 1184-1186. Zbl 0673.62033, MR 0997599, 10.2307/2290153
Reference: [6] Chesneau, C.: On the adaptive wavelet deconvolution of a density for strong mixing sequences.J. Korean Stat. Soc. 41 (2012), 423-436. Zbl 1296.62079, MR 3255347, 10.1016/j.jkss.2012.01.005
Reference: [7] Comte, F., Dedecker, J., Taupin, M.-L.: Adaptive density deconvolution with dependent inputs.Math. Methods Stat. 17 (2008), 87-112. Zbl 1282.62087, MR 2429122, 10.3103/S1066530708020014
Reference: [8] Comte, F., Rozenholc, Y., Taupin, M.-L.: Penalized contrast estimator for adaptive density deconvolution.Can. J. Stat. 34 (2006), 431-452. Zbl 1104.62033, MR 2328553, 10.1002/cjs.5550340305
Reference: [9] Dedecker, J., Prieur, C.: New dependence coefficients. Examples and applications to statistics.Probab. Theory Relat. Fields 132 (2005), 203-236. Zbl 1061.62058, MR 2199291, 10.1007/s00440-004-0394-3
Reference: [10] Delaigle, A., Meister, A.: Nonparametric function estimation under Fourier-oscillating noise.Stat. Sin. 21 (2011), 1065-1092. Zbl 1232.62057, MR 2827515, 10.5705/ss.2009.082
Reference: [11] Devroye, L.: Consistent deconvolution in density estimation.Can. J. Stat. 17 (1989), 235-239. Zbl 0679.62029, MR 1033106, 10.2307/3314852
Reference: [12] Esary, J. D., Proschan, F., Walkup, D. W.: Association of random variables, with applications.Ann. Math. Stat. 38 (1967), 1466-1474. Zbl 0183.21502, MR 0217826, 10.1214/aoms/1177698701
Reference: [13] Fan, J.: On the optimal rates of convergence for nonparametric deconvolution problems.Ann. Stat. 19 (1991), 1257-1272. Zbl 0729.62033, MR 1126324, 10.1214/aos/1176348248
Reference: [14] Fortuin, C. M., Kasteleyn, P. W., Ginibre, J.: Correlation inequalities on some partially ordered sets.Commun. Math. Phys. 22 (1971), 89-103. Zbl 0346.06011, MR 0309498, 10.1007/BF01651330
Reference: [15] Groeneboom, P., Jongbloed, G.: Density estimation in the uniform deconvolution model.Staat. Neerl. 57 (2003), 136-157. Zbl 1090.62527, MR 2035863, 10.1111/1467-9574.00225
Reference: [16] Hall, P., Meister, A.: A ridge-parameter approach to deconvolution.Ann. Stat. 35 (2007), 1535-1558. Zbl 1147.62031, MR 2351096, 10.1214/009053607000000028
Reference: [17] Lehmann, E. L.: Some concepts of dependence.Ann. Math. Stat. 37 (1966), 1137-1153. Zbl 0146.40601, MR 0202228, 10.1214/aoms/1177699260
Reference: [18] Levin, B. Y.: Lectures on Entire Functions.Translations of Mathematical Monographs 150. AMS, Providence (1996). Zbl 0856.30001, MR 1400006, 10.1090/mmono/150
Reference: [19] Liu, M. C., Taylor, R. L.: A consistent nonparametric density estimator for the deconvolution problem.Can. J. Stat. 17 (1989), 427-438. Zbl 0694.62017, MR 1047309, 10.2307/3315482
Reference: [20] Masry, E.: Multivariate probability density deconvolution for stationary random processes.IEEE Trans. Inf. Theory 37 (1991), 1105-1115. Zbl 0732.60045, MR 1111811, 10.1109/18.87002
Reference: [21] Masry, E.: Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes.J. Multivariate Anal. 44 (1993), 47-68. Zbl 0783.62065, MR 1208469, 10.1006/jmva.1993.1003
Reference: [22] Masry, E.: Strong consistency and rates for deconvolution of multivariate densities of stationary processes.Stochastic Processes Appl. 47 (1993), 53-74. Zbl 0797.62071, MR 1232852, 10.1016/0304-4149(93)90094-K
Reference: [23] Masry, E.: Deconvolving multivariate kernel density estimates from contaminated associated observations.IEEE Trans. Inf. Theory 49 (2003), 2941-2952. Zbl 1302.62085, MR 2027570, 10.1109/TIT.2003.818415
Reference: [24] Meister, A.: Deconvolution from Fourier-oscillating error densities under decay and smoothness restrictions.Inverse Probl. 24 (2008), Article ID 015003, 14 pages. Zbl 1143.65106, MR 2384762, 10.1088/0266-5611/24/1/015003
Reference: [25] Meister, A.: Deconvolution Problems in Nonparametric Statistics.Lecture Notes in Statistics 193. Springer, Berlin (2009). Zbl 1178.62028, MR 2768576, 10.1007/978-3-540-87557-4
Reference: [26] Oliveira, P. E.: Asymptotics for Associated Random Variables.Springer, Berlin (2012). Zbl 1249.62001, MR 3013874, 10.1007/978-3-642-25532-8
Reference: [27] Pan, J.: Tail dependence of random variables from ARCH and heavy-tailed bilinear models.Sci. China, Ser. A 45 (2002), 749-760. Zbl 1098.62549, MR 1915886, 10.1360/02ys9082
Reference: [28] Pensky, M., Vidakovic, B.: Adaptive wavelet estimator for nonparametric density deconvolution.Ann. Stat. 27 (1999), 2033-2053. Zbl 0962.62030, MR 1765627, 10.1214/aos/1017939249
Reference: [29] Rudin, W.: Real and Complex Analysis.McGraw-Hill, New York (1987). Zbl 0925.00005, MR 0924157
Reference: [30] Stefanski, L. A., Carroll, R. J.: Deconvoluting kernel density estimators.Statistics 21 (1990), 169-184. Zbl 0697.62035, MR 1054861, 10.1080/02331889008802238
Reference: [31] Es, B. van, Spreij, P., Zanten, H. van: Nonparametric volatility density estimation.Bernoulli 9 (2003), 451-465. Zbl 1044.62037, MR 1997492, 10.3150/bj/1065444813
Reference: [32] Zanten, H. van, Zareba, P.: A note on wavelet density deconvolution for weakly dependent data.Stat. Inference Stoch. Process. 11 (2008), 207-219. Zbl 1204.62051, MR 2403107, 10.1007/s11203-007-9013-0
Reference: [33] Volkonskij, V. A., Rozanov, Y. A.: Some limit theorems for random functions. I.Theor. Probab. Appl. 4 (1959), 178-197. Zbl 0092.33502, MR 0121856, 10.1137/1104015
.

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