Basic estimation of Markovian pseudo long-Range dependent processes

Robert, Stephan (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

The pseudo self similar processes are quite attractive due to their simplicity but the question we are interested in this paper concerns the basic estimation of such models. How do the standard estimators (sample mean and variance) converge with time? This will give us an indication about the time we have to collect data in order to accurately model them. With no surprise we notice that this is dependant of the Hurst parameter of course and on the number of states the model has (which defines the domain in which the behavior is self-similar). One has to collect more data with higher Hurst parameters and with more states in the Markov chain to accurately estimate the mean and variance of the process. Outside the domain where the process is self similar, standard statistics methods apply.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Pisa, Italy, 12 June 2009
Date:
2009-06
Pisa, Italy
12 June 2009
Pagination:
5 p.
Published in:
Proceedings of 2009 IEEE 14th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, 12 June 2009, Pisa, Italy
DOI:
ISBN:
978-1-4244-3532-6
Appears in Collection:



 Record created 2020-04-07, last modified 2020-10-27

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