A higher-order moment formula for non-zero-mean AR processes

Author

Ngo, Chiu Yeung ; Mendel, Jerry M.

Author_Institution

Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA, USA

fYear

1991

fDate

14-17 Apr 1991

Firstpage

3089

Abstract

An autoregressive (AR) model which is excited by a non-zero-mean, independent and identically distributed stationary random process is investigated. As in the zero-mean case, a cumulant-based higher-order Yule-Walker equation is derived. By expanding the cumulants in terms of their moments, a higher-order moment formula is obtained. This formula not only relates the higher-order moment with the lower-order moment, but also makes it possible to estimate the AR parameters and the output mean simultaneously. The formula is computationally more efficient than the cumulant formula

Keywords

random processes; signal processing; statistical analysis; cumulant expansion; cumulant-based higher-order Yule-Walker equation; higher-order moment; identically distributed stationary random process; independent random process; lower-order moment; nonzero mean autoregressive processes; output mean; signal processing; statistical analysis; Additive white noise; Equations; Gaussian noise; Gaussian processes; Image processing; Integrated circuit modeling; Maximum likelihood estimation; Parameter estimation; Random processes; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Conference_Location

Toronto, Ont.

ISSN

1520-6149

Print_ISBN

0-7803-0003-3

Type

conf

DOI

10.1109/ICASSP.1991.150108

Filename

150108