Nonparametric density estimators based on nonstationary absolutely regular random sequences

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dc.contributor.authorPuri, Madan L.
dc.contributor.authorHarel, Michel
dc.date.accessioned2018-05-03T17:41:41Z
dc.date.available2018-05-03T17:41:41Z
dc.date.issued1996
dc.descriptionPublisher's, offprint version
dc.description.abstractIn 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.
dc.identifier.citationPuri, M. L. “Nonparametric density estimators based on nonstationary absolutely regular random sequences.” Journal of Applied Mathematics and Stochastic Analysis (1996), Volume 9 Issue 3, 233–254. Co-author: Michel Harel.
dc.identifier.doihttps://doi.org/10.1155/S1048953396000238
dc.identifier.urihttps://hdl.handle.net/2022/22082
dc.language.isoen
dc.publisherJournal of Applied Mathematics and Stochastic Analysis
dc.relation.isversionofhttps://www.hindawi.com/journals/ijsa/1996/189689/abs/
dc.subjectDensity Estimators
dc.subjectNonstationary Absolutely Regular Random Sequences
dc.subjectStrong Mixing
dc.subjectp-Mixing
dc.subjectMarkov Processes
dc.subjectARMA Processes
dc.titleNonparametric density estimators based on nonstationary absolutely regular random sequences
dc.typeArticle

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