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

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 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.

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

Quantile spectral analysis and long-memory time series

1986 ◽  
Vol 23 (A) ◽  
pp. 41-54 ◽  
Author(s):  
Emanuel Parzen
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Long Memory ◽  
Model Identification ◽  
Time Series Model ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Memory Time ◽  
Short Memory ◽  
Simultaneous Use

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis. It proposes a framework to integrate the analysis of long-memory (non-stationary) time series with the analysis of short-memory (stationary) time series.

Quantile spectral analysis and long-memory time series

1986 ◽  
Vol 23 (A) ◽  
pp. 41-54 ◽  
Author(s):  
Emanuel Parzen
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Long Memory ◽  
Model Identification ◽  
Time Series Model ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Memory Time ◽  
Short Memory ◽  
Simultaneous Use

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis. It proposes a framework to integrate the analysis of long-memory (non-stationary) time series with the analysis of short-memory (stationary) time series.

scholarly journals FIXED-B ASYMPTOTICS FOR THE STUDENTIZED MEAN FROM TIME SERIES WITH SHORT, LONG, OR NEGATIVE MEMORY

2011 ◽  
Vol 28 (2) ◽  
pp. 471-481 ◽  
Author(s):  
Tucker McElroy ◽  
Dimitris N. Politis
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Long Memory ◽  
Variance Estimation ◽  
Simulation Studies ◽  
Sample Mean ◽  
Memory Time ◽  
Negative Memory ◽  
Short Memory ◽  
The Mean

This paper considers the problem of variance estimation for the sample mean in the context of long memory and negative memory time series dynamics, adopting the fixed-bandwidth approach now popular in the econometrics literature. The distribution theory generalizes the short memory results of Kiefer and Vogelsang (2005, Econometric Theory 21, 1130–1164). In particular, our results highlight the dependence on the kernel (we include flat-top kernels), whether or not the kernel is nonzero at the boundary, and, most important, whether or not the process is short memory. Simulation studies support the importance of accounting for memory in the construction of confidence intervals for the mean.

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