Preprint #97-13
Space-time data are ubiquitous in the environmental sciences. Often, as
is the case with atmospheric and oceanographic processes, these data
contain many different scales of spatial and temporal variability. Such
data are often non-stationary in space and time and may involve many
observation/prediction locations. These factors can limit the
effectiveness of traditional space-time statistical models and methods.
In this article, we propose the use of hierarchical space-time models to
achieve more flexible models and methods for the analysis of
environmental data distributed in space and time. The first stage of
the hierarchical model specifies a measurement-error process for the
observational data in terms of some ``state'' process. The second stage
allows for site-specific time series models for this state variable.
This stage includes large-scale (e.g., seasonal) variability plus a
space-time dynamic process for the ``anomalies''. Much of our interest
is with this anomaly process. In the third stage, the parameters of
these time series models, which are distributed in space, are themselves
given a joint distribution with spatial dependence (Markov random
fields). The Bayesian formulation is completed in the last two stages
by specifying priors on parameters. We implement the model in a Markov
chain Monte Carlo framework and apply it to an atmospheric data set of
monthly maximum temperature.
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Department
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