International audienceSkewness is often present in a wide range of geostatistical problems, and modeling it in the spatial context remains a challenging problem. In this article, we propose and study a new class of spatial skew-normal random fields, defined in terms of the closed multivariate skew-normal distribution. Such fields can be written as the sum of two independent fields: one Gaussian and the other truncated Gaussian. We derive theoretical expressions for the first- and second-order moments, and use them within a method of moments based procedure to estimate the parameters of the model. Data simulated from the model are used to illustrate the methodology developed
This paper proposes a new regression model for the analysis of spatial panel data in the case of spa...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
The univariate and multivariate skew-normal distributions have a number of intriguing properties. It...
International audienceSkewness is often present in a wide range of geostatistical problems, and mode...
In this paper we propose a spatial latent factor model to deal with multivariate geostatistical skew...
In this paper we propose a spatial latent factor model to deal with multivariate geostatistical skew...
Existing studies on spatial panel data models typically assume a normal distribution for the random ...
<p>This article develops Bayesian inference of spatial models with a flexible skew latent structure....
Summarization: The inverse problem of determining the spatial dependence of random fields from an ex...
In this paper, we propose a new class of non-Gaussian random fields named two-piece random fields. T...
Spartan random fields are special cases of Gibbs random fields. Their joint probability density func...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
AbstractIn this article, we introduce the concept of skewness to the Gaussian random field theory by...
This paper deals with an alternative approach to combine spatial dependence and stochastic frontier ...
Spatial generalized linear mixed models are common in applied statistics. Most users are satisfied u...
This paper proposes a new regression model for the analysis of spatial panel data in the case of spa...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
The univariate and multivariate skew-normal distributions have a number of intriguing properties. It...
International audienceSkewness is often present in a wide range of geostatistical problems, and mode...
In this paper we propose a spatial latent factor model to deal with multivariate geostatistical skew...
In this paper we propose a spatial latent factor model to deal with multivariate geostatistical skew...
Existing studies on spatial panel data models typically assume a normal distribution for the random ...
<p>This article develops Bayesian inference of spatial models with a flexible skew latent structure....
Summarization: The inverse problem of determining the spatial dependence of random fields from an ex...
In this paper, we propose a new class of non-Gaussian random fields named two-piece random fields. T...
Spartan random fields are special cases of Gibbs random fields. Their joint probability density func...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
AbstractIn this article, we introduce the concept of skewness to the Gaussian random field theory by...
This paper deals with an alternative approach to combine spatial dependence and stochastic frontier ...
Spatial generalized linear mixed models are common in applied statistics. Most users are satisfied u...
This paper proposes a new regression model for the analysis of spatial panel data in the case of spa...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
The univariate and multivariate skew-normal distributions have a number of intriguing properties. It...