Copyright © 2003 Elsevier LtdThe authors introduce the D-statistic for testing for a constant spatial mean in geostatistical applications, i.e. testing for the presence or absence of a global drift. The D-statistic can be calculated with sparse data and for different directions and a statistical test of hypothesis of constant spatial mean has been designed using the global standardized D-statistic. If, for a given data set, the null hypothesis of constant mean cannot be accepted, a model with a (non-constant) drift would be more appropriate. The estimated confidence level and estimated power of the D-statistic hypothesis test is studied using Monte Carlo simulation, with different assumptions about the random field, the drift, the scale of ...
We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial...
Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomen...
A new method based on distances for modeling continuous random data in Gaussian random fields is pre...
In spatial statistics in general, and in geostatistics in particular, the choice between a spatial m...
Geostatistics is a field of spatial statistics. This treats with realizations of stochastics process...
This paper reviews recent advances made in testing in spatial statistics and discussed at the Spatia...
Geostatistical simulation relies on the definition of a stochastic model (e.g. a random field charac...
Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibratin...
The distribution of a variable observed over a domain depends on the underlying process and also on...
International audienceThe distribution of a variable observed over a domain depends on the underlyin...
Envelope tests are a popular tool in goodness-of-fit testing in spatial statistics. These tests grap...
The correct conclusion about the assumptions concerning some phenomena can be obtained only through ...
We propose a significance test to determine if data on a regular d-dimensional grid can be assumed t...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrat...
We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial...
Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomen...
A new method based on distances for modeling continuous random data in Gaussian random fields is pre...
In spatial statistics in general, and in geostatistics in particular, the choice between a spatial m...
Geostatistics is a field of spatial statistics. This treats with realizations of stochastics process...
This paper reviews recent advances made in testing in spatial statistics and discussed at the Spatia...
Geostatistical simulation relies on the definition of a stochastic model (e.g. a random field charac...
Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibratin...
The distribution of a variable observed over a domain depends on the underlying process and also on...
International audienceThe distribution of a variable observed over a domain depends on the underlyin...
Envelope tests are a popular tool in goodness-of-fit testing in spatial statistics. These tests grap...
The correct conclusion about the assumptions concerning some phenomena can be obtained only through ...
We propose a significance test to determine if data on a regular d-dimensional grid can be assumed t...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrat...
We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial...
Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomen...
A new method based on distances for modeling continuous random data in Gaussian random fields is pre...