Higher-order accurate polyspectral estimation with flat-top lag-windows

Arthur Berg, Dimitris N. Politis

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Improved performance in higher-order spectral density estimation is achieved using a general class of infinite-order kernels. These estimates are asymptotically less biased but with the same order of variance as compared to the classical estimators with second-order kernels. A simple, data-dependent algorithm for selecting the bandwidth is introduced and is shown to be consistent with estimating the optimal bandwidth. The combination of the specialized family of kernels with the new bandwidth selection algorithm yields a considerably improved polyspectral estimator surpassing the performances of existing estimators using second-order kernels. Bispectral simulations with several standard models are used to demonstrate the enhanced performance with the proposed methodology.

Original languageEnglish (US)
Pages (from-to)477-498
Number of pages22
JournalAnnals of the Institute of Statistical Mathematics
Volume61
Issue number2
DOIs
StatePublished - Jun 1 2009

Fingerprint

Higher Order
kernel
Estimator
Spectral Density Estimation
Optimal Bandwidth
Bandwidth Selection
Dependent Data
Biased
Standard Model
Bandwidth
Methodology
Estimate
Demonstrate
Simulation
Family
Class

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

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Higher-order accurate polyspectral estimation with flat-top lag-windows. / Berg, Arthur; Politis, Dimitris N.

In: Annals of the Institute of Statistical Mathematics, Vol. 61, No. 2, 01.06.2009, p. 477-498.

Research output: Contribution to journalArticle

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