Statistical Inference for Stochastic Processes

, Volume 10, Issue 3, pp 209–221 | Cite as

Nonparametric density estimation for nonmixing approximable Stochastic Processes

  • Salim LardjaneEmail author


The author deals with nonparametric density estimation for stochastic processes which satisfy the L -approximability property. He considers a Parzen–Rosenblatt estimator of the density for general stationary L -approximable processes. He states conditions under which it is consistent and investigates its rate of convergence. Finally, he applies his results to general nonmixing linear processes and nonmixing nonlinear autoregressive processes.


Nonparametric density estimation Nonmixing processes Near Epoch Dependence Linear processes Autoregressive processes 


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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.Laboratoire de Statistique d’EnquêtesCREST-ENSAIBruzFrance

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