948 research outputs found
Modeling Non-Stationary Processes Through Dimension Expansion
In this paper, we propose a novel approach to modeling nonstationary spatial
fields. The proposed method works by expanding the geographic plane over which
these processes evolve into higher dimensional spaces, transforming and
clarifying complex patterns in the physical plane. By combining aspects of
multi-dimensional scaling, group lasso, and latent variables models, a
dimensionally sparse projection is found in which the originally nonstationary
field exhibits stationarity. Following a comparison with existing methods in a
simulated environment, dimension expansion is studied on a classic test-bed
data set historically used to study nonstationary models. Following this, we
explore the use of dimension expansion in modeling air pollution in the United
Kingdom, a process known to be strongly influenced by rural/urban effects,
amongst others, which gives rise to a nonstationary field
On potentially negative space time covariances obtained as sum of products of marginal ones
Most of the literature on spatio-temporal covariance models proposes structures
that are positive in the whole domain. However, problems of physical, biological or
medical nature need covariance models allowing for negative values or oscillations
from positive to negative values. In this paper we propose an easy-to-implement
and interpretable class of models that admits this type of covariances. We show
particular analytical examples that may be of interest in the biometrical contex
Some covariance models based on normal scale mixtures
Modelling spatio-temporal processes has become an important issue in current
research. Since Gaussian processes are essentially determined by their second
order structure, broad classes of covariance functions are of interest. Here, a
new class is described that merges and generalizes various models presented in
the literature, in particular models in Gneiting (J. Amer. Statist. Assoc. 97
(2002) 590--600) and Stein (Nonstationary spatial covariance functions (2005)
Univ. Chicago). Furthermore, new models and a multivariate extension are
introduced.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ226 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Extrapolation of Stationary Random Fields
We introduce basic statistical methods for the extrapolation of stationary
random fields. For square integrable fields, we set out basics of the kriging
extrapolation techniques. For (non--Gaussian) stable fields, which are known to
be heavy tailed, we describe further extrapolation methods and discuss their
properties. Two of them can be seen as direct generalizations of kriging.Comment: 52 pages, 25 figures. This is a review article, though Section 4 of
the article contains new results on the weak consistency of the extrapolation
methods as well as new extrapolation methods for -stable fields with
$0<\alpha\leq 1
Application-driven Sequential Designs for Simulation Experiments: Kriging Metamodeling
This paper proposes a novel method to select an experimental design for interpolation in simulation.Though the paper focuses on Kriging in deterministic simulation, the method also applies to other types of metamodels (besides Kriging), and to stochastic simulation.The paper focuses on simulations that require much computer time, so it is important to select a design with a small number of observations.The proposed method is therefore sequential.The novelty of the method is that it accounts for the specific input/output function of the particular simulation model at hand; i.e., the method is application-driven or customized.This customization is achieved through cross-validation and jackknifing.The new method is tested through two academic applications, which demonstrate that the method indeed gives better results than a design with a prefixed sample size.experimental design;simulation;interpolation;sampling;sensitivity analysis;metamodels
Model-based geostatistics: some issues in modelling and model diagnostics
Spatial modelling is examined in a model-based geostatistical context using the Gaussian linear mixed model in a likelihood framework. Complex spatial models developed provide practitioners with a practical and best-practice guide for spatial analysis. Adequate modelling theory and matrix algebra are provided to ground the methods demonstrated. A multivariate model over two time points and three-dimensional space is developed which is novel to the field of soil science. Soil organic carbon measurements at three soil depths and two time points from a cropping field with four soil classes are used. The spatial process is assessed for second-order stationarity and anisotropic correlation. Univariate spatial modelling is used to inform bivariate spatial modelling of pre- and post-harvest soil organic carbon at each soil depth. Bivariate modelling is extended to the multivariate level, where both time points and the three soil depths are incorporated in a single model to pool maximum information. A common correlation structure is tested and is supported for the response variable at each of the six time-depth combinations. Separable correlation structures are used for computational efficiency. The difficulty of estimating nugget effects suggests a sub-optimal sampling design. Preferred fitted models are all isotropic. Equations for predictions and the variance of prediction errors are extended from well-known results and maps of predicted values and variance of prediction errors are produced and show close correspondence with observed values. Finally, univariate models for spatially referenced seed counts from small sampling plots are examined within a Gaussian framework using Box-Cox transformations. The discrete nature of the data, small sample size and computational problems hamper model fitting. Anisotropy is examined using a variogram envelope diagnostic technique. ASReml-R software is shown to be a powerful analytical tool for spatial processes
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