Bayesian modeling and analysis for gradients in spatiotemporal processes.
Bayesian modeling and analysis for gradients in spatiotemporal processes.
复制标题
时空过程梯度的贝叶斯建模和分析。
DOI：
10.1111/biom.12305
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发表时间：
2015
期刊：
影响因子：
1.9
通讯作者：
Carlin,BradleyP
中科院分区：
文献类型：
--
作者：
Quick,Harrison;Banerjee,Sudipto;Carlin,BradleyP
Stochastic process models are widely employed for analyzing spatiotemporal datasets in various scientific disciplines including, but not limited to, environmental monitoring, ecological systems, forestry, hydrology, meteorology, and public health. After inferring on a spatiotemporal process for a given dataset, inferential interest may turn to estimating rates of change, orgradients, over space and time. This manuscript develops fully model-based inference on spatiotemporal gradients under continuous space, continuous time settings. Our contribution is to offer, within a flexible spatiotemporal process model setting, a framework to estimate arbitrary directional gradients over space at any given timepoint, temporal derivatives at any given spatial location and, finally, mixed spatiotemporal gradients that reflect rapid change in spatial gradients over time and vice-versa. We achieve such inference without compromising on rich and flexible spatiotemporal process models and use nonseparable covariance structures. We illustrate our methodology using a simulated data example and subsequently apply it to a dataset of daily PM2.5 concentrations in California, where the spatiotemporal gradient process reveals the effects of California's unique topography on pollution and detects the aftermath of a devastating series of wildfires.
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DOI：
10.1001/jama.1985.03350370099032
发表时间：
1985-04
期刊：
JAMA
影响因子：
--
作者：
S. Jencks
通讯作者：
S. Jencks
美国事实上的精神卫生服务体系：公共卫生视角。
影响因子：
--
作者：
D. Regier;I. D. Goldberg;Taube Ca
通讯作者：
Taube Ca
初级医疗保健中 RDC 精神障碍的估计患病率
影响因子：
2
作者：
E. Hoeper;G. Nycz;P. Cleary;D. Regier;I. D. Goldberg
通讯作者：
I. D. Goldberg
影响因子：
--
作者：
M. Von Korff;S. Shapiro;J. Burke;M. Teitlebaum;E. Skinner;P. German;R. W. Turner;L. Klein;B. Burns
通讯作者：
B. Burns
影响因子：
105.7
作者：
GOLDBERG, DP;BLACKWELL, B
通讯作者：
BLACKWELL, B