Validation of Regression Kriging

What is cross validation in kriging?

Cross validation is often used for testing “moving neighborhood” kriging models; in this case, each unknown value is predicted from a small number of surrounding data. In “unique neighborhood” kriging algorithms, each estimation uses all the available data; as a result, cross validation would spend much computer time.

How do you calculate kriging variance?

Kriging – unbiasedness



Z(s) = m + e(s), where e(s) is a zero mean second-order stationary random field with covariogram function C(h) and variogram g(h). Also ²=C(0).

Is regression a kriging?

Regression Kriging is a spatial interpolation technique that combines a regression of the dependent variable on predictors (i.e. the environmental covariates) with kriging of the prediction residuals.

How is regression kriging different from universal kriging?

Regression kriging assumes that the fitted trend surface is the correct polynomial (not only the correct degree but also the correct coefficients) whereas Universal kriging only assumes that the trend surface determines the maximum degree of the polynomial but does not use the coefficients from the Trend surface.
 

What is validation in geostatistics?

Validation first removes part of the data (call it the test dataset). It then uses the rest of the data (call it the training dataset) to develop the trend and autocorrelation models to be used for prediction. In Geostatistical Analyst, you create the test and training datasets using the Subset Features tool.

How do you cross validate?

What is cross-validation?

1. Divide the dataset into two parts: one for training, other for testing.
2. Train the model on the training set.
3. Validate the model on the test set.
4. Repeat 1-3 steps a couple of times. This number depends on the CV method that you are using.

How do you interpret kriging?

Kriging can be understood as a two-step process: first, the spatial covariance structure of the sampled points is determined by fitting a variogram; and second, weights derived from this covariance structure are used to interpolate values for unsampled points or blocks across the spatial field.

What is the limitation of kriging?

Limitations of Kriging Interpolation



Kriging assumes that the space being studied is stationary; that is to say, that the joint probability distribution doesn’t change throughout the study space. It also assumes a property called isotropy; that there is uniformity in every direction.

Is kriging more accurate than IDW?

Quote from video:

What is cross-validation?

Definition. Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model.

What is cross-validation for?

Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.
 

What are cross-validation methods in geostatistics?

“Cross Validation” allows us to compare estimated and true values using the information available in our sample data set. The sample values are temporarily discarded from the sample data set; the value is then estimated using the remaining samples. The estimates are then compared to the true values.