Publication

Analysis of Rainfall Data by Robust Spatial Statistics using S+SpatialStats

1998
Journal Articles

Abstract

This paper discusses the use of robust geostatistical methods on a data set of rainfall measurements in Switzerland. The variables are detrended via non-parametric estimation penalized with a smoothing parameter. The optimal trend is computed with a smoothing parameter based on cross-validation. Then, the variogram is estimated by a highly robust estimator of scale. The parametric variogram model is fitted by generalized least squares, thus taking account of the variance-covariance structure of the variogram estimates. Comparison of kriging with the initial measurements is completed and yields interesting results. All these computations are done with the software S+SpatialStats, extended with new functions in Splus that are made available.

Official source

https://infoscience.epfl.ch/entities/publication/2ea49afc-6454-4619-a3c2-8cf9ecf7f52c

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1998
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2ea49afc-6454-4619-a3c2-8cf9ecf7f52c

About this result

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Variogram

In spatial statistics the theoretical variogram, denoted , is a function describing the degree of spatial dependence of a spatial random field or stochastic process . The semivariogram is half the variogram. In the case of a concrete example from the field of gold mining, a variogram will give a measure of how much two samples taken from the mining area will vary in gold percentage depending on the distance between those samples. Samples taken far apart will vary more than samples taken close to each other.

Robust regression

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations).

Related publications (10)

Analysis of Rainfall Data by Robust Spatial Statistics using S+SpatialStats

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