Bartlett Correction of Empirical Likelihood for Non-Gaussian Short-Memory Time Series

2016 ◽  
Vol 37 (5) ◽  
pp. 624-649 ◽  
Author(s):  
Kun Chen ◽  
Ngai Hang Chan ◽  
Chun Yip Yau
Keyword(s):  
Time Series ◽  
Empirical Likelihood ◽  
Bartlett Correction ◽  
Memory Time ◽  
Short Memory ◽  
Non Gaussian

scholarly journals Spatial long memory

2019 ◽  
Vol 3 (1) ◽  
pp. 243-256
Author(s):  
Peter M. Robinson
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Spatial Data ◽  
Long Memory ◽  
The Other ◽  
Future Prospects ◽  
Memory Time ◽  
Short Memory ◽  
The One ◽  
Relevant Work

AbstractWe discuss developments and future prospects for statistical modeling and inference for spatial data that have long memory. While a number of contributons have been made, the literature is relatively small and scattered, compared to the literatures on long memory time series on the one hand, and spatial data with short memory on the other. Thus, over several topics, our discussions frequently begin by surveying relevant work in these areas that might be extended in a long memory spatial setting.

scholarly journals Confidence Regions for Parameters in Stationary Time Series Models With Gaussian Noise

2022 ◽  
Vol 9 ◽  
Author(s):  
Xiuzhen Zhang ◽  
Riquan Zhang ◽  
Zhiping Lu
Keyword(s):  
Time Series ◽  
Empirical Likelihood ◽  
Long Memory ◽  
Score Function ◽  
Real Data ◽  
Confidence Regions ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Finite Sample ◽  
Memory Time

This article develops two new empirical likelihood methods for long-memory time series models based on adjusted empirical likelihood and mean empirical likelihood. By application of Whittle likelihood, one obtains a score function that can be viewed as the estimating equation of the parameters of the long-memory time series model. An empirical likelihood ratio is obtained which is shown to be asymptotically chi-square distributed. It can be used to construct confidence regions. By adding pseudo samples, we simultaneously eliminate the non-definition of the original empirical likelihood and enhance the coverage probability. Finite sample properties of the empirical likelihood confidence regions are explored through Monte Carlo simulation, and some real data applications are carried out.

scholarly journals Residual empirical processes for long and short memory time series

2008 ◽  
Vol 36 (5) ◽  
pp. 2453-2470 ◽  
Author(s):  
Ngai Hang Chan ◽  
Shiqing Ling
Keyword(s):  
Time Series ◽  
Empirical Processes ◽  
Memory Time ◽  
Short Memory

scholarly journals Correction: Residual empirical processes for long and short memory time series

2010 ◽  
Vol 38 (6) ◽  
pp. 3839-3839
Author(s):  
Ngai Hang Chan ◽  
Shiqing Ling
Keyword(s):  
Time Series ◽  
Empirical Processes ◽  
Memory Time ◽  
Short Memory

Estimatingd for long and short memory time series

1994 ◽  
Vol 5 (3) ◽  
pp. 255-271 ◽  
Author(s):  
Gareth Janacek
Keyword(s):  
Time Series ◽  
Memory Time ◽  
Short Memory
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