A statistical inference method for a subset of long-range dependent FARIMA processes

Author

Mossberg, Magnus

Author_Institution

Dept. of Phys. & Electr. Eng., Karlstad Univ., Karlstad, Sweden

fYear

2012

fDate

5-8 Aug. 2012

Firstpage

456

Lastpage

459

Abstract

A subset of long-range dependent FARIMA processes is considered. A method for estimating the parameter that describes the long-range dependency of such a process is suggested. The method is based on an asymptotic expression for the covariance function of the process and gives a closed form solution by means of a weighted linear least squares estimate. The variance of the estimate given by themethod is analyzed and, at the same time, the optimal choice of the weighting is expressed. A numerical illustration of the method and the material in the paper is provided.

Keywords

autoregressive moving average processes; least squares approximations; statistical analysis; asymptotic expression; covariance function; fractional autoregressive integrated moving average process; long-range dependent FARIMA processes subset; numerical illustration; statistical inference method; weighted linear least squares estimate; Covariance matrix; Equations; Estimation; Least squares approximation; Monte Carlo methods; Reactive power; Estimation; FARIMA process; long-range dependency;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing Workshop (SSP), 2012 IEEE

Conference_Location

Ann Arbor, MI

ISSN

pending

Print_ISBN

978-1-4673-0182-4

Electronic_ISBN

pending

Type

conf

DOI

10.1109/SSP.2012.6319730

Filename

6319730