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Generalized Whittle Estimate For Nonstationary Spatial Data

  • Yasumasa Matsuda
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    This paper considers analysis of nonstationary irregularly spaced data that may have multivariate observations. The nonstationarity we focus on here means a local dependency of parameters that describe covariance structures. Nonparametric and parametric ways to estimate the local dependency of the parameters are proposed by an extension of traditional periodogram for stationary time series to that for nonstationary spatial data We introduce locally stationary processes for which consistency of the estimators are proved as well as demonstrate empirical efficiency of the methods by simulated and real examples.

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    File URL: http://hdl.handle.net/10097/56546
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    File URL: http://ir.library.tohoku.ac.jp/re/bitstream/10097/56546/1/terg305.pdf
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    Paper provided by Graduate School of Economics and Management, Tohoku University in its series TERG Discussion Papers with number 305.

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    Length: 20 pages
    Date of creation: May 2013
    Date of revision:
    Handle: RePEc:toh:tergaa:305
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    Kawauchi, Aoba-ku, Sendai 980-8476

    Web page: http://www.econ.tohoku.ac.jp/econ/english/index.html
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    1. Yun Bai & Peter X.-K. Song & T. E. Raghunathan, 2012. "Joint composite estimating functions in spatiotemporal models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(5), pages 799-824, November.
    2. Yasumasa Matsuda & Yoshihiro Yajima, 2009. "Fourier analysis of irregularly spaced data on "R"-super-"d"," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 191-217.
    3. Michael L. Stein & Zhiyi Chi & Leah J. Welty, 2004. "Approximating likelihoods for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 275-296.
    4. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848.
    5. IM, Hae Kyung & Stein, Michael L. & Zhu, Zhengyuan, 2007. "Semiparametric Estimation of Spectral Density With Irregular Observations," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 726-735, June.
    6. Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
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