کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11027492 | 1666271 | 2018 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
P-spline smoothing for spatial data collected worldwide
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موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علوم زمین و سیاره ای (عمومی)
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چکیده انگلیسی
Spatial data collected worldwide from a huge number of locations is frequently used in environmental and climate studies. Spatial modelling for this type of data presents both methodological and computational challenges. In this work we illustrate a computationally efficient non-parametric framework in order to model and estimate the spatial field while accounting for geodesic distances between locations. The spatial field is modelled via penalized splines (P-splines) using intrinsic Gaussian Markov Random Field (GMRF) priors for the spline coefficients. The key idea is to use the sphere as a surrogate for the Globe, then build the basis of B-spline functions on a geodesic grid system. The basis matrix is sparse as is the precision matrix of the GMRF prior, thus computational efficiency is gained by construction. We illustrate the approach with a real climate study, where the goal is to identify the Intertropical Convergence Zone using high-resolution remote sensing data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Spatial Statistics - Volume 27, October 2018, Pages 1-17
Journal: Spatial Statistics - Volume 27, October 2018, Pages 1-17
نویسندگان
Fedele Greco, Massimo Ventrucci, Elisa Castelli,