Issue
ESAIM: PS
Volume 6, 2002
New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
Page(s) 271 - 292
Section New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
DOI https://doi.org/10.1051/ps:2002015
Published online 15 November 2002
  1. R.J. Carroll and P. Hall, Optimal rates of convergence for deconvolving a density. J. Amer. Statist. Assoc. 83 (1988) 1184-1186. [CrossRef] [MathSciNet]
  2. L. Devroye, Consistent deconvolution in density estimation. Canad. J. Statist. 17 (1989) 235-239. [CrossRef] [MathSciNet]
  3. J. Fan, Asymptotic normality for deconvolution kernel density estimators. Sankhya Ser. A 53 (1991) 97-110. [MathSciNet]
  4. J. Fan, Global behavior of deconvolution kernel estimates. Statist. Sinica 1 (1991) 541-551. [MathSciNet]
  5. J. Fan, On the optimal rates of convergence for nonparametric deconvolution problems. Ann. Statist. 19 (1991) 1257-1272. [CrossRef] [MathSciNet]
  6. J. Fan, Adaptively local one-dimensional subproblems with application to a deconvolution problem. Ann. Statist. 21 (1993) 600-610. [CrossRef] [MathSciNet]
  7. W. Feller, An introduction to probability theory and its applications, Vol. II. John Wiley & Sons Inc., New York (1971).
  8. R.D. Gill and B.Y. Levit, Applications of the Van Trees inequality: A Bayesian Cramér-Rao bound. Bernoulli 1 (1995) 59-79. [CrossRef] [MathSciNet]
  9. H. Ishwaran, Information in semiparametric mixtures of exponential families. Ann. Statist. 27 (1999) 159-177. [CrossRef] [MathSciNet]
  10. B.G. Lindsay, Exponential family mixture models (with least-squares estimators). Ann. Statist. 14 (1986) 124-137. [CrossRef] [MathSciNet]
  11. M.C. Liu and R.L. Taylor, A consistent nonparametric density estimator for the deconvolution problem. Canad. J. Statist. 17 (1989) 427-438. [CrossRef] [MathSciNet]
  12. C. Matias and M.-L. Taupin, Minimax estimation of some linear functionals in the convolution model, Manuscript. Université Paris-Sud (2001).
  13. P. Medgyessy, Decomposition of superposition of density functions on discrete distributions. II. Magyar Tud. Akad. Mat. Fiz. Oszt. Közl. 21 (1973) 261-382.
  14. M.H. Neumann, On the effect of estimating the error density in nonparametric deconvolution. J. Nonparametr. Statist. 7 (1997) 307-330. [CrossRef] [MathSciNet]
  15. M. Pensky and B. Vidakovic, Adaptive wavelet estimator for nonparametric density deconvolution. Ann. Statist. 27 (1999) 2033-2053. [CrossRef] [MathSciNet]
  16. L. Stefanski and R.J. Carroll, Deconvoluting kernel density estimators. Statistics 21 (1990) 169-184. [CrossRef] [MathSciNet]
  17. L.A. Stefanski, Rates of convergence of some estimators in a class of deconvolution problems. Statist. Probab. Lett. 9 (1990) 229-235. [CrossRef] [MathSciNet]
  18. M.L. Taupin. Semi-parametric estimation in the non-linear errors-in-variables model. Ann. Statist. 29 (2001) 66-93.
  19. A.W. van der Vaart, Asymptotic statistics. Cambridge University Press, Cambridge (1998).
  20. A.W. van der Vaart and J.A. Wellner, Weak convergence and empirical processes. Springer-Verlag, New York (1996). With applications to statistics.
  21. C.-H. Zhang, Fourier methods for estimating mixing densities and distributions. Ann. Statist. 18 (1990) 806-831. [CrossRef] [MathSciNet]

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