Spatial-depth functional estimation of ocean temperature from non-separable covariance models

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• 作者：R. M. Espejo ; R. Fernández-Pascual…
• 关键词：Bayesian estimation ; Empirical orthogonal functions ; Moment ; based estimation ; Ocean temperature ; Spatial ; depth functional regression ; Spatial ; depth functional covariates
• 刊名：Stochastic Environmental Research and Risk Assessment
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：31
• 期：1
• 页码：39-51
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Math. Appl. in Environmental Science; Earth Sciences, general; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Computa
• 出版者：Springer Berlin Heidelberg
• ISSN：1436-3259
• 卷排序：31

文摘

Spatial-depth functional regression is applied for the estimation of ocean temperature, with projection onto the eigenvectors of the empirical covariance operator of the functional response (i.e., onto the Empirical Orthogonal Functions in space and depth). Moment-based estimation is performed to approximate the regression operators in the subspace generated by the empirical eigenvectors associated with nonnull eigenvalues. In addition, Bayesian estimation is performed to approximate the regression operators in the subspace generated by the empirical eigenvectors associated with almost null eigenvalues. The cross-validation results obtained, together with the spatial-depth residual correlation analysis carried out on a real data set for the South Atlantic area, to the east of Argentina and the Falkland Islands, represent an improvement on those provided by the wavelet-based approach recently proposed in Fernández-Pascual (Stoch Environ Res Risk Assess 30:523–557, 2016).