Free Access
Issue
ESAIM: PS
Volume 13, January 2009
Page(s) 135 - 151
DOI https://doi.org/10.1051/ps:2008001
Published online 26 March 2009
  1. K.B. Athreya and G.S. Atuncar, Kernel estimation for real-valued Markov chains. Sankhyā Ser. A 60 (1998) 1–17. [MathSciNet]
  2. P.J. Bickel, C.A.J. Klaassen, Y. Ritov and J.A. Wellner, Efficient and Adaptive Estimation for Semiparametric Models. Springer, New York (1998).
  3. F.C. Drost, C.A.J. Klaassen and B.J.M. Werker, Adaptive estimation in time-series models. Ann. Statist. 25 (1997) 786–817. [CrossRef] [MathSciNet]
  4. E.W. Frees, Estimating densities of functions of observations. J. Amer. Statist. Assoc. 89 (1994) 517–525. [CrossRef] [MathSciNet]
  5. E. Giné and D. Mason, On local U-statistic processes and the estimation of densities of functions of several variables. Ann. Statist. 35 (2007a) 1105–1145. [CrossRef] [MathSciNet]
  6. E. Giné and D. Mason, Laws of the iterated logarithm for the local U-statistic process. J. Theoret. Probab. 20 (2007b) 457–485. [CrossRef] [MathSciNet]
  7. P. Jeganathan, Some aspects of asymptotic theory with applications to time series models. Econometric Theory 11 (1995) 818–887. [CrossRef] [MathSciNet]
  8. H.L. Koul and A. Schick, Efficient estimation in nonlinear autoregressive time series models. Bernoulli 3 (1997) 247–277. [CrossRef] [MathSciNet]
  9. J.-P. Kreiss, On adaptive estimation in stationary ARMA processes. Ann. Statist. 1 (1987a) 112–133. [CrossRef]
  10. J.-P. Kreiss, On adaptive estimation in autoregressive models when there are nuisance functions. Statist. Decisions 5 (1987b) 59–76. [MathSciNet]
  11. M. Ledoux and M. Talagrand, Probability in Banach Spaces. Isoperimetry and Processes. Ergebnisse der Mathematik und ihrer Grenzgebiete (3) 23, Springer, Berlin (1991).
  12. U.U. Müller, A. Schick and W. Wefelmeyer, Weighted residual-based density estimators for nonlinear autoregressive models. Statist. Sinica 15 (2005) 177–195. [MathSciNet]
  13. H.T. Nguyen, Recursive nonparametric estimation in stationary Markov processes. Publ. Inst. Statist. Univ. Paris 29 (1984) 65–84. [MathSciNet]
  14. A.B. Owen, Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75 (1988) 237–249. [CrossRef] [MathSciNet]
  15. A.B. Owen, Empirical Likelihood. Monographs on Statistics and Applied Probability 92, Chapman & Hall / CRC, London (2001).
  16. G.G. Roussas, Nonparametric estimation in Markov processes. Ann. Inst. Statist. Math. 21 (1969) 73–87. [CrossRef] [MathSciNet]
  17. G.G. Roussas, Nonparametric estimation in mixing sequences of random variables. J. Statist. Plann. Inference 18 (1988). 135–149.
  18. A. Saavedra and R. Cao, Rate of convergence of a convolution-type estimator of the marginal density of an MA(1) process. Stoch. Proc. Appl. 80 (1999) 129–155. [CrossRef]
  19. A. Saavedra and R. Cao, On the estimation of the marginal density of a moving average process. Canad. J. Statist. 28 (2000) 799–815. [CrossRef] [MathSciNet]
  20. A. Schick and W. Wefelmeyer, Root-n consistent and optimal density estimators for moving average processes. Scand. J. Statist. 31 (2004a) 63–78. [CrossRef] [MathSciNet]
  21. A. Schick and W. Wefelmeyer, Root-n consistent density estimators for sums of independent random variables. J. Nonparametr. Statist. 16 (2004b) 925–935. [CrossRef]
  22. A. Schick and W. Wefelmeyer, Functional convergence and optimality of plug-in estimators for stationary densities of moving average processes. Bernoulli 10 (2004c) 889–917. [CrossRef] [MathSciNet]
  23. A. Schick and W. Wefelmeyer, Convergence rates in weighted L1-spaces of kernel density estimators for linear processes. Technical Report, Department of Mathematical Sciences, Binghamton University (2006).
  24. A. Schick and W. Wefelmeyer, Uniformly root-n consistent density estimators for weakly dependent invertible linear processes. Ann. Statist. 35 (2007a) 815–843. [CrossRef] [MathSciNet]
  25. A. Schick and W. Wefelmeyer, Root-n consistent density estimators of convolutions in weighted L1-norms. J. Statist. Plann. Inference 137 (2007b) 1765–1774. [CrossRef] [MathSciNet]
  26. A. Schick and W. Wefelmeyer, Root-n consistency in weighted L1-spaces for density estimators of invertible linear processes. in: Stat. Inference Stoch. Process. 11 (2008) 281–310.

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