Nonparametric density estimators based on nonstationary absolutely regular random sequences

DOI10.1155/S1048953396000238zbMath0897.62036MaRDI QIDQ1815747

Michel Harel, Madan Lal Puri

Publication date: 18 November 1996

Published in: Journal of Applied Mathematics and Stochastic Analysis (Search for Journal in Brave)

Full work available at URL: https://eudml.org/doc/47991

zbMATH Keywords

strong mixingcentral limit theoremsARMA processesdensity estimatorsphi-mixingnonstationary absolutely regular random sequences

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05) Markov processes: estimation; hidden Markov models (62M05)

Related Items (3)

The marked empirical process to test nonlinear time series against a large class of alternatives when the random vectors are nonstationary and absolutely regular ⋮ Central limit theorem of the smoothed empirical distribution functions for asymptotically stationary absolutely regular stochastic processes ⋮ Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields

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