An asymptotic theory for spectral analysis of random fields Academic Article

abstract

• 2017, Institute of Mathematical Statistics. All rights reserved. For a general class of stationary random fields we study asymptotic properties of the discrete Fourier transform (DFT), periodogram, parametric and nonparametric spectral density estimators under an easily verifiable short-range dependence condition expressed in terms of functional dependence measures. We allow irregularly spaced data which is indexed by a subset of Zd. Our asymptotic theory requires minimal restriction on the index set . Asymptotic normality is derived for kernel spectral density estimators and the Whittle estimator of a parameterized spectral density function. We also develop asymptotic results for a covariance matrix estimate.

authors

• Pourahmadi, Mohsen

published proceedings

• ELECTRONIC JOURNAL OF STATISTICS

author list (cited authors)

• Deb, S., Pourahmadi, M., & Wu, W. B.

citation count

• 8

complete list of authors

• Deb, Soudeep||Pourahmadi, Mohsen||Wu, Wei Biao

publication date

• January 2017

publisher

• Institute of Mathematical Statistics  Publisher

published in

• Electronic Journal of Statistics  Journal

keywords

• Irregular Spaced Data
• Random Fields
• Spectral Density
• Time Series

Digital Object Identifier (DOI)

• 10.1214/17-EJS1326

start page

• 4297

end page

• 4322

volume

• 11

issue

• 2

URL

• http://dx.doi.org/10.1214/17-ejs1326