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2009
Geostatistics is a popular class of statistical methods for estimating, or predicting, the value of a continuous spatial process at unobserved locations given the value of the process at a set of known locations. Spatial prediction of this sort is typically performed using the method known as kriging, which provides estimates that are optimized over ...
Calder, Catherine, Cressie, Noel A
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Geostatistics is a popular class of statistical methods for estimating, or predicting, the value of a continuous spatial process at unobserved locations given the value of the process at a set of known locations. Spatial prediction of this sort is typically performed using the method known as kriging, which provides estimates that are optimized over ...
Calder, Catherine, Cressie, Noel A
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New Approach by Kriging Models to Problems in QSAR
Journal of Chemical Information and Computer Sciences, 2004Most models in quantitative structure and activity relationship (QSAR) research, proposed by various techniques such as ordinary least squares regression, principal components regression, partial least squares regression, and multivariate adaptive regression splines, involve a linear parametric part and a random error part.
Kai-Tai Fang, Hong Yin, Yi-Zeng Liang
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Geostatistical models and kriging
IFAC Proceedings Volumes, 2003Abstract Geostatistics is an application of the theory of random functions to spatially distributed data. Geostatistical methods like kriging were initially proposed in mining and petroleum exploration and found their way back to mainstream statistics more than a decade ago.
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51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference<BR> 18th AIAA/ASME/AHS Adaptive Structures Conference<BR> 12th, 2010
Kriging models have proven useful in estimating complex and computationally expensive analyses. They are capable of interpolating a set of observations by quantifying both longer range variations with parametric trends and shorter range variations with spatial correlations.
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Kriging models have proven useful in estimating complex and computationally expensive analyses. They are capable of interpolating a set of observations by quantifying both longer range variations with parametric trends and shorter range variations with spatial correlations.
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Kriging models for microwave filters
2016 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2016Surrogate modeling of microwave filters' response is discussed. In particular, kriging is used to model either the scattering parameters of the filter or the rational representation of the filter's characteristics. Surrogate models for these two variants of kriging are validated in solving a microwave filter optimization problem.
Natalia Leszczynska +4 more
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Three dimensional variogram modeling and Kriging
2020In this research, semi-variograms of different directions, are used to form a three-dimensional variogram surface. A simple and practical surface generation technique, namely bilinear surface fitting method is used for this purpose. Co-variance between any of two sample points (drill holes) can be found on the surface easily. Angle and distance between
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Comparison of predictions by kriging and spatial autoregressive models
Communications in Statistics - Simulation and Computation, 2018ABSTRACTIn geostatistics, the prediction of unknown quantities at given locations is commonly made by the kriging technique.
Azadeh Mojiri +3 more
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Optimal Model Average Prediction in Orthogonal Kriging Models
Journal of Systems Science and ComplexityzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jun Wang +3 more
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Journal of Environmental Management, 2020
Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models ...
Ali Asghar, Rostami +3 more
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Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models ...
Ali Asghar, Rostami +3 more
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On Using Kriging Models as Probabilistic Models in Design
SAE Technical Paper Series, 2004<div class="htmlview paragraph">Kriging models are frequently used as metamodels during system design optimization. In many applications, a kriging model is used as a deterministic model of a computationally expensive analysis or simulation. In this paper, a kriging model is employed as a probabilistic model on a one-dimensional and two two ...
Jay D. Martin, Timothy W. Simpson
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