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All issues Volume 13 (January 2009) ESAIM: PS, 13 (2009) 135-151 References
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] [Google Scholar]
  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). [Google Scholar]
  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] [Google Scholar]
  4. E.W. Frees, Estimating densities of functions of observations. J. Amer. Statist. Assoc. 89 (1994) 517–525. [CrossRef] [MathSciNet] [Google Scholar]
  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] [Google Scholar]
  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] [Google Scholar]
  7. P. Jeganathan, Some aspects of asymptotic theory with applications to time series models. Econometric Theory 11 (1995) 818–887. [CrossRef] [MathSciNet] [Google Scholar]
  8. H.L. Koul and A. Schick, Efficient estimation in nonlinear autoregressive time series models. Bernoulli 3 (1997) 247–277. [CrossRef] [MathSciNet] [Google Scholar]
  9. J.-P. Kreiss, On adaptive estimation in stationary ARMA processes. Ann. Statist. 1 (1987a) 112–133. [CrossRef] [Google Scholar]
  10. J.-P. Kreiss, On adaptive estimation in autoregressive models when there are nuisance functions. Statist. Decisions 5 (1987b) 59–76. [MathSciNet] [Google Scholar]
  11. M. Ledoux and M. Talagrand, Probability in Banach Spaces. Isoperimetry and Processes. Ergebnisse der Mathematik und ihrer Grenzgebiete (3) 23, Springer, Berlin (1991). [Google Scholar]
  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] [Google Scholar]
  13. H.T. Nguyen, Recursive nonparametric estimation in stationary Markov processes. Publ. Inst. Statist. Univ. Paris 29 (1984) 65–84. [MathSciNet] [Google Scholar]
  14. A.B. Owen, Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75 (1988) 237–249. [CrossRef] [MathSciNet] [Google Scholar]
  15. A.B. Owen, Empirical Likelihood. Monographs on Statistics and Applied Probability 92, Chapman & Hall / CRC, London (2001). [Google Scholar]
  16. G.G. Roussas, Nonparametric estimation in Markov processes. Ann. Inst. Statist. Math. 21 (1969) 73–87. [CrossRef] [MathSciNet] [Google Scholar]
  17. G.G. Roussas, Nonparametric estimation in mixing sequences of random variables. J. Statist. Plann. Inference 18 (1988). 135–149. [Google Scholar]
  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] [Google Scholar]
  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] [Google Scholar]
  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] [Google Scholar]
  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] [Google Scholar]
  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] [Google Scholar]
  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). [Google Scholar]
  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] [Google Scholar]
  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] [Google Scholar]
  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. [Google Scholar]

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