Simulating a class of stationary Gaussian processes using the Davies-Harte algorithm, with application to long memory processes
We demonstrate that the fast and exact Davies-Harte algorithm is valid for simulating a certain class of stationary Gaussian processes - those with a negative autocovariance sequence for all non-zero lags. The result applies to well known classes of long memory processes: Gaussian fractionally differenced (FD) processes, fractional Gaussian noise (fGn) and the nonstationary fractional Brownian Motion (fBm). Copyright 2003 Blackwell Publishing Ltd.
Year of publication: |
2003
|
---|---|
Authors: | CRAIGMILE, PETER F. |
Published in: |
Journal of Time Series Analysis. - Wiley Blackwell, ISSN 0143-9782. - Vol. 24.2003, 5, p. 505-511
|
Publisher: |
Wiley Blackwell |
Saved in:
freely available
Saved in favorites
Similar items by person
-
All of Statistics: A Concise Course in Statistical Inference. Larry Wasserman
Craigmile, Peter F., (2005)
-
Spaceātime modelling of trends in temperature series
Craigmile, Peter F., (2011)
-
Craigmile, Peter F., (2004)
- More ...