Nonparametric density estimators based on nonstationary absolutely regular random sequences
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Journal of Applied Mathematics and Stochastic Analysis
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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.
Description
Publisher's, offprint version
Keywords
Density Estimators, Nonstationary Absolutely Regular Random Sequences, Strong Mixing, p-Mixing, Markov Processes, ARMA Processes
Citation
Puri, 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.