Parameter Estimation in an Autoregression Model with Infinite Variance

Author

Alexandr, Markov

Author_Institution

Tomsk State Univ., Tomsk

fYear

2008

fDate

18-20 June 2008

Firstpage

586

Lastpage

586

Abstract

A weighted least squares procedure is proposed for parameter estimation in an autoregression model of first order with infinite variance of the noise. It is assumed that the noise distribution function belongs to the stable domain of attraction with index alpha, 0 < alpha < 2. The proposed procedure is shown to have higher rate of convergence to true value of the parameter as compared with usual least squares estimate. The limit distribution for weighted least squares estimates has been derived. The results of numerical simulations are given.

Keywords

autoregressive processes; convergence of numerical methods; least squares approximations; parameter estimation; regression analysis; statistical distributions; autoregression model; convergence rate; infinite noise variance; limit distribution; noise distribution function; parameter estimation; weighted least squares estimation procedure; Autoregressive processes; Convergence; Distribution functions; Least squares approximation; Least squares methods; Numerical simulation; Parameter estimation; Probability distribution; Random variables; Stochastic resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on

Conference_Location

Dalian, Liaoning

Print_ISBN

978-0-7695-3161-8

Electronic_ISBN

978-0-7695-3161-8

Type

conf

DOI

10.1109/ICICIC.2008.414

Filename

4603775