NDLI: Identification of model components for a class of continuous spatiotemporal models

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Identification of model components for a class of continuous spatiotemporal models

Content Provider	Scilit
Author	Hartfield, Molly I. Gunst, Richard F.
Copyright Year	2003
Description	Environmental data routinely are collected at irregularly spaced monitoring stations and at intermittent times, times which may differ by location. This article introduces a class of continuous-time, continuous-space statistical models that can accommodate many of these more complex environmental processes. This class of models in corporates temporal and spatial variability in a cohesive manner and is broad enough to include temporal processes that are assumed to be generated by stochastic differential equations with possibly temporally and spatially correlated errors. A wide range of ARIMA temporal models and geostatistical spatial models are included in the class of models investigated. Techniques for identifying the structure of the temporal and spatial components of this class of models are detailed. Point estimates of model parameters, asymptotic distributions, and Kalman-filter prediction methods are discussed.
Related Links	http://link.springer.com/content/pdf/10.1198%2F1085711031175.pdf
Ending Page	121
Page Count	17
Starting Page	105
ISSN	10857117
e-ISSN	15372693
DOI	10.1198/1085711031175
Journal	Journal of Agricultural, Biological and Environmental Statistics
Issue Number	1
Volume Number	8
Language	English
Publisher	Informa UK Limited
Publisher Date	2003-03-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Agricultural, Biological and Environmental Statistics Mathematical Psychology Reml Arima Kalmanlter Kriging Geostatistics Variogram. Intrinsic Random Functions Asymptotic Distribution Random Function Spatial Correlation Point Estimation Spatial Variability Statistical Model Stochastic Differential Equation Kalman Filter
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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