Generalized Whittle Estimate For Nonstationary Spatial Data
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.
|Date of creation:||May 2013|
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- 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.
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- 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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