Model
Interpolation
Create surfaces from sample data using these interpolation methods:
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Inverse distance weighted
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Radial-based functions, which include the following kernels
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Thin plate spline
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Spline with tension
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Multiquadratic
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Inverse multiquadratic
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Completely regularized spline kernels
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Global and local polynomials
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Kriging for exact data and for error-contaminated data
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Ordinary, for data with unknown constant mean value
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Simple, for data with known mean value
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Universal, for data with mean value as a function on coordinates
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Indicator, for discrete data or data transformed to discrete
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Probability, for discrete data as primary variable and continuous data as secondary variables
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Disjunctive, for nonlinear predictions
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Cokriging (multivariate version of the above-mentioned kriging models)
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Isotropical or anisotropical models
Kriging Output Surface Types
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Prediction
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Prediction standard error (measure of the prediction quality)
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Probability map (probability that specified threshold value is exceeded)
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Error of indicators (measure of the probability map uncertainty)
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Quantile map (over- and underpredicted values)
Modeling Tools for Kriging
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Data transformations
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Box–Cox
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Logarithmic
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Arcsine
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Normal score
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Data detrending
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Global polynomial
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Local polynomial
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Variography
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Models (four can be used simultaneously)
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Nugget
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Circular
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Spherical
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Tetraspherical
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Pentaspherical
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Exponential
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Gaussian
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Rational quadratic
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Hole effect
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K-Bessel
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J-Bessel
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Stable
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Semivariogram/Covariance surface
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Anisotropy
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Specifying or estimating the proportion of measurement error in the nugget
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Cross-covariance option for shift between variables
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Estimation of all or part of the model parameters by a modified weighted least squares algorithm
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Declustering
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Checking for data bivariate distribution
Searching Neighborhood
To select neighboring data to predict the value for the target point
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Ellipse with one, four, or eight angular sectors
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Minimum and maximum number of points in each sector