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Nonparametric density estimators based on nonstationary absolutely regular random sequences

By Michel Harel and Madan L. Puri

Abstract

In this paper, the central limit theorems for the density estimator and for the integrated square error are proved for the case when the underlying sequence of random variables is nonstationary. Applications to Markov processes and ARMA processes are provided

Topics: density estimators, nonstationary absolutely regular random sequences, strong mixing, φ-mixing, Markov processes, ARMA processes., Science, Q, Mathematics, QA1-939
Publisher: Hindawi Limited
Year: 1996
DOI identifier: 10.1155/S1048953396000238
OAI identifier: oai:doaj.org/article:06a1764320674f9a917a064e218b1acc
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