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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/77042


    Title: A group lasso approach for non-stationary spatial–temporal covariance estimation
    Authors: Hsu, Nan-Jung;Chang, Ya-Mei;Huang, Hsin-Cheng
    Contributors: 淡江大學統計學系
    Keywords: coordinate descent;Frobenius loss;group lasso;Kalman filter;penalized least squares;spatial prediction
    Date: 2012-02
    Issue Date: 2012-05-24 15:17:49 (UTC+8)
    Publisher: Chichester: John Wiley & Sons Ltd.
    Abstract: We develop a new approach for modeling non-stationary spatial–temporal processes on the basis of data sampled at fixed locations over time. The approach applies a basis function formulation and a constrained penalized least squares method recently proposed for estimating non-stationary spatial-only covariance functions. In this article, we further incorporate the temporal dependence into this framework and model the spatial–temporal process as the sum of a spatial–temporal stationary process and a linear combination of known basis functions with temporal dependent coefficients. A group lasso penalty is devised to select the basis functions and estimate the parameters simultaneously. In addition, a blockwise coordinate descent algorithm is applied for implementation. This algorithm computes the constrained penalized least squares solutions along a regularization path very rapidly. The resulting dynamic model has a state-space form, thereby the optimal spatial–temporal predictions can be computed efficiently using the Kalman filter. Moreover, the methodology is applied to a wind speed data set observed at the western Pacific Ocean for illustration.
    Relation: Environmetrics 23(1), pp.12–23
    DOI: 10.1002/env.1130
    Appears in Collections:[統計學系暨研究所] 期刊論文

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