On modeling positive continuous data with spatiotemporal dependence

DOI10.1002/env.2632zbMath1545.62709MaRDI QIDQ6626180

Moreno Bevilacqua, Carlo Gaetan, Christian Caamaño-Carrillo

Publication date: 28 October 2024

Published in: Environmetrics (Search for Journal in Brave)

zbMATH Keywords

copulalinear predictionpairwise likelihoodregression modelnon-Gaussian datawind speed data

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (2)

Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-\(hh\) random fields estimation ⋮ Families of complex-valued covariance models through integration

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Comparing composite likelihood methods based on pairs for spatial Gaussian random fields
• Strictly and non-strictly positive definite functions on spheres
• Bivariate extreme statistics. I
• A multivariate gamma distribution arising from a Markov model
• Interpolation of spatial data. Some theory for kriging
• Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics
• Gaussian copula marginal regression
• Copula-based geostatistical modeling of continuous and discrete data including covariates
• A case study competition among methods for analyzing large spatial data
• Dependence modelling for spatial extremes
• Geostatistical Modelling Using Non-Gaussian Matérn Fields
• Dependence Modeling with Copulas
• A Latent Gaussian Markov Random-Field Model for Spatiotemporal Rainfall Disaggregation
• On Optimal Point and Block Prediction in Log-Gaussian Random Fields
• A note on composite likelihood inference and model selection
• Bayesian Prediction of Transformed Gaussian Random Fields
• A Composite Likelihood Approach to Binary Spatial Data
• $\chi^2$ Random Fields in Space and Time
• Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach
• Copula‐based semiparametric models for spatiotemporal data
• Strictly Proper Scoring Rules, Prediction, and Estimation
• A Multivariate Gamma-Type Distribution
• Dependence measures for extreme value analyses
• Gaussian linear state‐space model for wind fields in the North‐East Atlantic
• Non-Gaussian autoregressive processes with Tukey \(g\)-and-\(h\) transformations

This page was built for publication: On modeling positive continuous data with spatiotemporal dependence