Premium
Subsampling the mean of heavy‐tailed dependent observations
Author(s) -
Kokoszka Piotr,
Wolf Michael
Publication year - 2004
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1046/j.0143-9782.2003.00346.x
Subject(s) - mathematics , series (stratigraphy) , statistics , selection (genetic algorithm) , autoregressive conditional heteroskedasticity , heavy tailed distribution , confidence interval , econometrics , marginal distribution , focus (optics) , point process , computer science , machine learning , random variable , volatility (finance) , paleontology , biology , physics , optics
. We establish the validity of subsampling confidence intervals for the mean of a dependent series with heavy‐tailed marginal distributions. Using point process theory, we focus on GARCH‐like time series models. We propose a data‐dependent method for the optimal block size selection and investigate its performance by means of a simulation study.