Electronic Journal of Statistics

An asymptotic theory for spectral analysis of random fields

Soudeep Deb, Mohsen Pourahmadi, and Wei Biao Wu

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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 $\Gamma $ of $\mathbb{Z}^{d}$. Our asymptotic theory requires minimal restriction on the index set $\Gamma $. 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.

Article information

Electron. J. Statist., Volume 11, Number 2 (2017), 4297-4322.

Received: March 2017
First available in Project Euclid: 13 November 2017

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Time series random fields irregular spaced data spectral density

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Deb, Soudeep; Pourahmadi, Mohsen; Wu, Wei Biao. An asymptotic theory for spectral analysis of random fields. Electron. J. Statist. 11 (2017), no. 2, 4297--4322. doi:10.1214/17-EJS1326. https://projecteuclid.org/euclid.ejs/1510563632

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