Log-periodogram regression for non-parametric estimation of spectral density for a non-Gaussian series

G. Fay; E. Moulines; Ph Soulier

Log-periodogram regression for non-parametric estimation of spectral density for a non-Gaussian series

G. Fay
, E. Moulines
, Ph Soulier

Telecom Paris

Research output: Contribution to conference › Paper › peer-review

Abstract

In this contribution, we consider the non-parametric estimation of the spectral density of a non-Gaussian linear process. The proposed method is a projection estimation of the log-density via regression on the log-periodogram. A data driven order selection is performed, and the asymptotic optimality with respect to the average square error criterion is proven for non-Gaussian linear processes. Finally, a central limit theorem on the cepstral coefficients estimates is given.

Original language	English
Pages	332-335
Number of pages	4
Publication status	Published - 1 Dec 1998
Event	Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing - Portland, OR, USA Duration: 14 Sept 1998 → 16 Sept 1998

Conference

Conference	Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing
City	Portland, OR, USA
Period	14/09/98 → 16/09/98

Cite this

@conference{14b044c183334fd8b70bfacf44ad1522,

title = "Log-periodogram regression for non-parametric estimation of spectral density for a non-Gaussian series",

abstract = "In this contribution, we consider the non-parametric estimation of the spectral density of a non-Gaussian linear process. The proposed method is a projection estimation of the log-density via regression on the log-periodogram. A data driven order selection is performed, and the asymptotic optimality with respect to the average square error criterion is proven for non-Gaussian linear processes. Finally, a central limit theorem on the cepstral coefficients estimates is given.",

author = "G. Fay and E. Moulines and Ph Soulier",

year = "1998",

month = dec,

day = "1",

language = "English",

pages = "332--335",

note = "Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing ; Conference date: 14-09-1998 Through 16-09-1998",

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TY - CONF

T1 - Log-periodogram regression for non-parametric estimation of spectral density for a non-Gaussian series

AU - Fay, G.

AU - Moulines, E.

AU - Soulier, Ph

PY - 1998/12/1

Y1 - 1998/12/1

N2 - In this contribution, we consider the non-parametric estimation of the spectral density of a non-Gaussian linear process. The proposed method is a projection estimation of the log-density via regression on the log-periodogram. A data driven order selection is performed, and the asymptotic optimality with respect to the average square error criterion is proven for non-Gaussian linear processes. Finally, a central limit theorem on the cepstral coefficients estimates is given.

AB - In this contribution, we consider the non-parametric estimation of the spectral density of a non-Gaussian linear process. The proposed method is a projection estimation of the log-density via regression on the log-periodogram. A data driven order selection is performed, and the asymptotic optimality with respect to the average square error criterion is proven for non-Gaussian linear processes. Finally, a central limit theorem on the cepstral coefficients estimates is given.

M3 - Paper

AN - SCOPUS:0032299469

SP - 332

EP - 335

T2 - Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing

Y2 - 14 September 1998 through 16 September 1998

ER -

Log-periodogram regression for non-parametric estimation of spectral density for a non-Gaussian series

Abstract

Conference

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Cite this