Recursive methods for estimating multiple missing values of a multivariate stationary process

Author

Bondon, Pascal ; Ruiz, Diego P. ; Gallego, Antolino

Author_Institution

Lab. des Signaux et Syst., CNRS, Gif-sur-Yvette, France

Volume

3

fYear

1998

fDate

12-15 May 1998

Firstpage

1361

Abstract

Existing methods for estimating linearly s future values of a m-variate stationary random process using a record of p vectors from the past consist in first solving the one-step prediction problem and then all the h-step prediction problems for 2⩽h⩽s independently. When the Levinson (1947) algorithm is used, each prediction problem is solved with a numerical complexity proportional to p². We propose new methods to solve the h-step prediction problems for h⩾2 with a numerical complexity proportional to p

Keywords

computational complexity; prediction theory; random processes; recursive estimation; signal processing; Levinson algorithm; multiple missing values estimation; multivariate stationary process; numerical complexity; prediction problems; recursive methods; signal processing; stationary random process; vectors; Bonding; Geophysical signal processing; Geophysics; Hilbert space; Predictive models; Random processes; Recursive estimation; Signal analysis; Signal processing algorithms; Space stations;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681699

Filename

681699