User menu

Nonparametric estimation for dependent data

Bibliographic reference Johannes, Jan ; Subba Roa, Suhasini. Nonparametric estimation for dependent data. In: Journal of Nonparametric Statistics, Vol. 23, no.3, p. 661-681 (2011)
Permanent URL
  1. Athreya Krishna B., Pantula Sastry G., Mixing Properties of Harris Chains and Autoregressive Processes, 10.2307/3214462
  2. Basrak Bojan, Davis Richard A., Mikosch Thomas, Regular variation of GARCH processes, 10.1016/s0304-4149(01)00156-9
  3. Bosq D., Nonparametric Statistics for Stochastic Processes, ISBN:9780387985909, 10.1007/978-1-4612-1718-3
  4. Bousamma, F. 1998. “Ergodicité, mélange et estimation dans les modèles GARCH”. PhD Thesis, Paris 7
  5. Bradley Richard C., A covariance inequality under a two-part dependence assumption, 10.1016/0167-7152(95)00231-6
  6. Bradley R. C., Introduction to Strong Mixing Conditions Volumes 1,2 and 3 (2007)
  7. Bryk ¶ Artur, Mielniczuk ¶ Jan, Asymptotic properties of density estimates for linear processes: application of projection method, 10.1080/10488525042000267770
  8. Chanda K. C., Strong Mixing Properties of Linear Stochastic Processes, 10.2307/3212764
  9. Cheng B., Statistica Sinica, 1, 335 (1991)
  10. Cheng Bing, Robinson P.M, Semiparametric estimation from time series with long-range dependence, 10.1016/0304-4076(94)90068-x
  11. Claeskens Gerda, Hall Peter, Effect of dependence on stochastic measures of accuracy of density estimations, 10.1214/aos/1021379860
  12. Cline D., Statistica Sinica, 9, 1103 (1999)
  13. Csorgo Sandor, Mielniczuk Jan, Nonparametric Regression Under Long-Range Dependent Normal Errors, 10.1214/aos/1176324633
  14. Csörgö Sándor, Mielniczuk Jan, Csorgo Sandor, Random-Design Regression under Long-Range Dependent Errors, 10.2307/3318432
  15. Csörgö S., Statistica Sinica, 10, 771 (2001)
  16. Davidson James, Stochastic Limit Theory, ISBN:9780198774037, 10.1093/0198774036.001.0001
  17. Doukhan P., Mixing, Properties and Examples (1994)
  18. Estévez G., Vieu† P., Nonparametric estimation under long memory dependence, 10.1080/10485250310001604668
  19. Fan J., Nonlinear Time Series: Nonparametric and Parametric Models (2005)
  20. Fryzlewicz, P. and Subba Rao, S. 2011. “On Mixing Properties of ARCH and Time-Varying ARCH Processes”. Forthcoming Bernoulli
  21. Geweke John, Porter-Hudak Susan, THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS, 10.1111/j.1467-9892.1983.tb00371.x
  22. Giraitis Liudas, Kokoszka Piotr, Leipus Remigijus, STATIONARY ARCH MODELS: DEPENDENCE STRUCTURE AND CENTRAL LIMIT THEOREM, 10.1017/s0266466600161018
  23. Giraitis Liudas, Koul Hira L, Surgailis Donatas, Asymptotic normality of regression estimators with long memory errors, 10.1016/0167-7152(95)00188-3
  24. Gorodetskii V. V., On the Strong Mixing Property for Linear Sequences, 10.1137/1122049
  25. Hall Peter, Hart Jeffrey D., Convergence rates in density estimation for data from infinite-order moving average processes, 10.1007/bf01198432
  26. Hall Peter, Hart Jeffrey D., Nonparametric regression with long-range dependence, 10.1016/0304-4149(90)90100-7
  27. Hall P., Martingale Limit Theory and its Application (1980)
  28. Hall Peter, Lahiri Soumendra Nath, Truong Young K., On bandwidth choice for density estimation with dependent data, 10.1214/aos/1034713655
  29. Hart J., Journal of the American Statistical Association, 83, 86 (1984)
  30. Hart J., Journal of the Royal Statistical Society (B), 56, 529 (1994)
  31. Künsch, H. R. Statistical Aspects Of Self-Similar Processes. Proceedings of the World Congress of the Bernoulli Society, Vol. 1, pp.67–74.
  32. Linton O., Mammen E., Estimating Semiparametric ARCH(oo) Models by Kernel Smoothing Methods1, 10.1111/j.1468-0262.2005.00596.x
  33. Masry Elias, Tjøstheim Dag, Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality, 10.1017/s0266466600009166
  34. Mielniczuk Jan, On the asymptotic mean integrated squared error of a kernel density estimator for dependent data, 10.1016/s0167-7152(96)00165-4
  35. Dinh Tuan Pham, The mixing property of bilinear and generalised random coefficient autoregressive models, 10.1016/0304-4149(86)90042-6
  36. Rio E., Annales de l'Institut Henri Poincaré Probablités. Statistiques, 29, 587 (1993)
  37. Rio Emmanuel, About the Lindeberg method for strongly mixing sequences, 10.1051/ps:1997102
  38. Robinson P. M., NONPARAMETRIC ESTIMATORS FOR TIME SERIES, 10.1111/j.1467-9892.1983.tb00368.x
  39. Robinson P.M., Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression, 10.1016/0304-4076(91)90078-r
  40. Robinson P. M., Log-Periodogram Regression of Time Series with Long Range Dependence, 10.1214/aos/1176324636
  41. Robinson P. M., Root-N-Consistent Semiparametric Regression, 10.2307/1912705
  42. Rosenblatt M., Non-parametric Techniques in Statistical Inference, 199 (1970)
  43. Multivariate Density Estimation, ISBN:9780470316849, 10.1002/9780470316849
  44. Subba Rao S., Sankhya, 68, 600 (2006)
  45. Viano M. C., Deniau Cl., Oppenheim G., LONG-RANGE DEPENDENCE AND MIXING FOR DISCRETE TIME FRACTIONAL PROCESSES, 10.1111/j.1467-9892.1995.tb00237.x
  46. Vieu Philippe, Quadratic errors for nonparametric estimates under dependence, 10.1016/0047-259x(91)90105-b