Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 576-599 |
| Seitenumfang | 24 |
| Fachzeitschrift | Journal of time series analysis |
| Jahrgang | 28 |
| Ausgabenummer | 4 |
| Frühes Online-Datum | 18 Jan. 2007 |
| Publikationsstatus | Veröffentlicht - Juli 2007 |
Abstract
This paper considers blockwise empirical likelihood for real-valued linear time processes which may exhibit either short- or long-range dependence. Empirical likelihood approaches intended for weakly dependent time series can fail in the presence of strong dependence. However, a modified blockwise method is proposed for confidence interval estimation of the process mean, which is valid for various dependence structures including long-range dependence. The finite-sample performance of the method is evaluated through a simulation study and compared with other confidence interval procedures involving subsampling or normal approximations.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Entscheidungswissenschaften (insg.)
- Statistik, Wahrscheinlichkeit und Ungewissheit
- Mathematik (insg.)
- Angewandte Mathematik
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in: Journal of time series analysis, Jahrgang 28, Nr. 4, 07.2007, S. 576-599.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Empirical likelihood confidence intervals for the mean of a long-range dependent process
AU - Nordman, Daniel J.
AU - Sibbertsen, Philipp
AU - Lahiri, Soumendra N.
PY - 2007/7
Y1 - 2007/7
N2 - This paper considers blockwise empirical likelihood for real-valued linear time processes which may exhibit either short- or long-range dependence. Empirical likelihood approaches intended for weakly dependent time series can fail in the presence of strong dependence. However, a modified blockwise method is proposed for confidence interval estimation of the process mean, which is valid for various dependence structures including long-range dependence. The finite-sample performance of the method is evaluated through a simulation study and compared with other confidence interval procedures involving subsampling or normal approximations.
AB - This paper considers blockwise empirical likelihood for real-valued linear time processes which may exhibit either short- or long-range dependence. Empirical likelihood approaches intended for weakly dependent time series can fail in the presence of strong dependence. However, a modified blockwise method is proposed for confidence interval estimation of the process mean, which is valid for various dependence structures including long-range dependence. The finite-sample performance of the method is evaluated through a simulation study and compared with other confidence interval procedures involving subsampling or normal approximations.
KW - Blocking
KW - Confidence interval
KW - Empirical likelihood
KW - FARIMA
KW - Long-range dependence
UR - http://www.scopus.com/inward/record.url?scp=34250635445&partnerID=8YFLogxK
U2 - 10.1111/j.1467-9892.2006.00526.x
DO - 10.1111/j.1467-9892.2006.00526.x
M3 - Article
AN - SCOPUS:34250635445
VL - 28
SP - 576
EP - 599
JO - Journal of time series analysis
JF - Journal of time series analysis
SN - 0143-9782
IS - 4
ER -