کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1064569 | 1485792 | 2014 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatio-temporal graphical modeling with innovations based on multi-scale diffusion kernel
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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
A random field of interest is observed on an undirected spatial graph over time, thereby providing a time series of dependent random fields. We propose a general modeling procedure which has the potential to explicitly quantify intrinsic and extrinsic fluctuations of such dynamical system. We adopt a paradigm in which the intrinsic fluctuations correspond to a process of latent diffusion on the graph arising from stochastic interactions within the system, whereas the extrinsic fluctuations correspond to a temporal drift reflecting the effects of the environment on the system. We start with a spatio-temporal diffusion process which gives rise to the latent spatial process. This makes a bridge with the conventional Wold representation, for which the latent process represents the innovation process, and beyond that with the stochastic differential equation associated to the Fokker-Planck dynamic. The innovation process is modeled by a Gaussian distribution whose covariance matrix is defined by a multi-scale diffusion kernel. This model leads to a multi-scale representation of the spatio-temporal process. We propose a statistical procedure to estimate the multi-scale structure and the model parameters in the case of the vector autoregressive model with drift. Modeling and estimation tasks are illustrated on simulated and real biological data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Spatial Statistics - Volume 7, February 2014, Pages 40-61
Journal: Spatial Statistics - Volume 7, February 2014, Pages 40-61
نویسندگان
Bernard Chalmond,