Non‐Gaussian geostatistical modeling using (skew) t processes-Reference-Cited by-同舟云学术

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Non‐Gaussian geostatistical modeling using (skew) t processes

Published:2020-02-17 Issue:1 Volume:48 Page:212-245
ISSN:0303-6898
Container-title:Scandinavian Journal of Statistics
language:en
Short-container-title:Scand J Statist

Author:

Bevilacqua Moreno¹²^ORCID,Caamaño‐Carrillo Christian³,Arellano‐Valle Reinaldo B.⁴,Morales‐Oñate Víctor⁵

Affiliation:

1. Departamento de Estadística Universidad de Valparaíso

2. Millennium Nucleus Center for the Discovery of Structures in Complex Data

3. Departamento de Estadística Universidad del Bío‐Bío

4. Departamento de Estadística Pontificia Universidad Católica de Chile

5. Departamento de Desarrollo Ambiente y Territorio, Facultad Latinoamericana de Ciencias Sociales

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/sjos.12447

Reference76 articles.

1. Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere

2. A latent Gaussian Markov random-field model for spatiotemporal rainfall disaggregation

3. The centred parameterization and related quantities of the skew-t distribution

4. On some characterizations of the t-distribution

5. Student-t censored regression model: properties and inference

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3. Skew Multiple Scaled Mixtures of Normal Distributions with Flexible Tail Behavior and Their Application to Clustering;Journal of Classification;2024-05-06

4. Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation;Computational Statistics & Data Analysis;2024-03

5. Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes;Bayesian Analysis;2023-12-01

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