Details
| Original language | English |
|---|---|
| Pages (from-to) | 576-599 |
| Number of pages | 24 |
| Journal | Journal of time series analysis |
| Volume | 28 |
| Issue number | 4 |
| Early online date | 18 Jan 2007 |
| Publication status | Published - Jul 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.
Keywords
- Blocking, Confidence interval, Empirical likelihood, FARIMA, Long-range dependence
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Mathematics(all)
- Applied Mathematics
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In: Journal of time series analysis, Vol. 28, No. 4, 07.2007, p. 576-599.
Research output: Contribution to journal › Article › Research › 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 -