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Stationary bootstrap for kernel density estimators under ψ-weak dependence

Title
Stationary bootstrap for kernel density estimators under ψ-weak dependence
Authors
Hwang E.Shin D.W.
Ewha Authors
신동완황은주
SCOPUS Author ID
신동완scopus
Issue Date
2012
Journal Title
Computational Statistics and Data Analysis
ISSN
0167-9473JCR Link
Citation
vol. 56, no. 6, pp. 1581 - 1593
Indexed
SCIE; SCOPUS WOS scopus
Abstract
Stationary bootstrap technique is applied for kernel-type estimators of densities and their derivatives of stationary ψ-weakly dependent processes. The ψ-weak dependence, introduced by Doukhan & Louhichi [Doukhan, P.; Louhichi, S.; 1999. A new weak dependence condition and applications to moment inequalities. Stochastic Processes and their Applications 84, 313342], unifies weak dependence conditions such as mixing, association, Gaussian sequences and Bernoulli shifts. The class of ψ-weakly dependent processes includes all weakly dependent processes of interest in statistics, containing such important processes as GARCH processes, threshold autoregressive processes, and bilinear processes. We obtain asymptotic validity for the stationary bootstrap in the density and derivatives estimation. A Monte-Carlo experiment compares the proposed method with other methods. Log returns of daily Dow Jones index are analyzed by the proposed method. © 2011 Elsevier B.V. All rights reserved.
DOI
10.1016/j.csda.2011.10.001
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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