4,343 research outputs found
Combining domain knowledge and statistical models in time series analysis
This paper describes a new approach to time series modeling that combines
subject-matter knowledge of the system dynamics with statistical techniques in
time series analysis and regression. Applications to American option pricing
and the Canadian lynx data are given to illustrate this approach.Comment: Published at http://dx.doi.org/10.1214/074921706000001049 in the IMS
Lecture Notes Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
Spatial Random Field Models Inspired from Statistical Physics with Applications in the Geosciences
The spatial structure of fluctuations in spatially inhomogeneous processes
can be modeled in terms of Gibbs random fields. A local low energy estimator
(LLEE) is proposed for the interpolation (prediction) of such processes at
points where observations are not available. The LLEE approximates the spatial
dependence of the data and the unknown values at the estimation points by
low-lying excitations of a suitable energy functional. It is shown that the
LLEE is a linear, unbiased, non-exact estimator. In addition, an expression for
the uncertainty (standard deviation) of the estimate is derived.Comment: 10 pages, to appear in Physica A v4: Some typos corrected and
grammatical change
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