Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso

DOI10.1080/10618600.2020.1811103OpenAlexW3072492331MaRDI QIDQ5066402

Stephen R. Becker, William Kleiber, Mitchell Krock

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2020.1811103

zbMATH Keywords

graphical modelgraphical Lassospatial basis functions

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks ⋮ Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso

Uses Software

• GMRFLib
• glasso
• QUIC
• spcov
• FRK

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Sparse estimation of a covariance matrix
• Sparse inverse covariance estimation with the graphical lasso
• Latent variable graphical model selection via convex optimization
• Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
• Asymptotic properties of discrete Fourier transforms for spatial data
• Interpolation of spatial data. Some theory for kriging
• Convex analysis approach to d. c. programming: Theory, algorithms and applications
• Pathwise coordinate optimization
• High-dimensional graphs and variable selection with the Lasso
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Emergence of Scaling in Random Networks
• Multiresolution models for nonstationary spatial covariance functions
• Model selection and estimation in the Gaussian graphical model
• Fourier Analysis of Irregularly Spaced Data onRd
• Fixed Rank Kriging for Very Large Spatial Data Sets
• Gaussian Predictive Process Models for Large Spatial Data Sets
• 10.1162/15324430152748236
• Spectral methods for nonstationary spatial processes
• Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets
• Gaussian Markov Random Fields
• Strictly Proper Scoring Rules, Prediction, and Estimation
• An Introduction to Matrix Concentration Inequalities

This page was built for publication: Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso