DocumentCode
2028154
Title
A statistical study of a regularized method for long auto-regressive spectral estimation
Author
Giovannelli, Jean-François ; Demoment, Guy
Author_Institution
Lab. des Signaux et Syst., Gif-sur-Yvette, France
Volume
4
fYear
1993
fDate
27-30 April 1993
Firstpage
137
Abstract
The authors address the problem of power spectral density estimation of time series with auto-regressive (AR) models when only a short span of data is available for analysis. The AR coefficients are estimated through a regularized method proposed by G. Kitagawa and W. Gersch (1985). An experimental study of this method and a comparison with the classical least squares (LS) method are outlined. The principles of the statistical study and computation results are presented.<>
Keywords
parameter estimation; signal processing; spectral analysis; statistical analysis; time series; long auto-regressive spectral estimation; power spectral density; regularized method; statistical study;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location
Minneapolis, MN, USA
ISSN
1520-6149
Print_ISBN
0-7803-7402-9
Type
conf
DOI
10.1109/ICASSP.1993.319613
Filename
319613
Link To Document