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  • 标题:An asymptotic theory for spectral analysis of random fields
  • 本地全文:下载
  • 作者:Soudeep Deb ; Mohsen Pourahmadi ; Wei Biao Wu
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2017
  • 卷号:11
  • 期号:2
  • 页码:4297-4322
  • DOI:10.1214/17-EJS1326
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要: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.
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