A group lasso approach for non-stationary spatial–temporal covariance estimation
學年 100
學期 2
出版（發表）日期 2012-02-01
作品名稱 A group lasso approach for non-stationary spatial–temporal covariance estimation
作品名稱（其他語言）
著者 Hsu, Nan-Jung; Chang, Ya-Mei; Huang, Hsin-Cheng
單位 淡江大學統計學系
出版者 Chichester: John Wiley & Sons Ltd.
著錄名稱、卷期、頁數 Environmetrics 23(1), pp.12–23
摘要 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.
關鍵字 coordinate descent; Frobenius loss; group lasso; Kalman filter; penalized least squares; spatial prediction
語言 en
ISSN 1099-095X
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Hsu, Nan-Jung
審稿制度
國別 GBR
公開徵稿
出版型式 電子版
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