Spectral Methods in Spatial Statistics
Joint Authors
Zhang, Lianmin
Chen, Kun
Pan, Maolin
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-15
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
When the spatial location area increases becoming extremely large, it is very difficult, if not possible, to evaluate the covariance matrix determined by the set of location distance even for gridded stationary Gaussian process.
To alleviate the numerical challenges, we construct a nonparametric estimator called periodogram of spatial version to represent the sample property in frequency domain, because periodogram requires less computational operation by fast Fourier transform algorithm.
Under some regularity conditions on the process, we investigate the asymptotic unbiasedness property of periodogram as estimator of the spectral density function and achieve the convergence rate.
American Psychological Association (APA)
Chen, Kun& Zhang, Lianmin& Pan, Maolin. 2014. Spectral Methods in Spatial Statistics. Discrete Dynamics in Nature and Society،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-467480
Modern Language Association (MLA)
Chen, Kun…[et al.]. Spectral Methods in Spatial Statistics. Discrete Dynamics in Nature and Society No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-467480
American Medical Association (AMA)
Chen, Kun& Zhang, Lianmin& Pan, Maolin. Spectral Methods in Spatial Statistics. Discrete Dynamics in Nature and Society. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-467480
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-467480