TY - JOUR
T1 - Stationary bootstrap for kernel density estimators under ψ-weak dependence
AU - Hwang, Eunju
AU - Shin, Dong Wan
N1 - Funding Information:
The authors are very grateful for the constructive and valuable comments of two anonymous referees. This work was supported by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( 2009-0093827 ).
PY - 2012/6
Y1 - 2012/6
N2 - 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.
AB - 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.
KW - Density
KW - Derivative
KW - Kernel estimator
KW - Stationary bootstrap
KW - Weak dependence
UR - http://www.scopus.com/inward/record.url?scp=84857656199&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2011.10.001
DO - 10.1016/j.csda.2011.10.001
M3 - Article
AN - SCOPUS:84857656199
SN - 0167-9473
VL - 56
SP - 1581
EP - 1593
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 6
ER -