Estimating spatial covariance using penalised likelihood with weightedL1penalty

DOI10.1080/10485250903023632zbMath1172.62032OpenAlexW1974324645WikidataQ60521649 ScholiaQ60521649MaRDI QIDQ3182743

Yu Feng Liu, Zhengyuan Zhu

Publication date: 16 October 2009

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250903023632

zbMATH Keywords

maximum likelihoodnonstationarityCholesky decompositionLASSOGaussian Markov random fields

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Asymptotic properties of parametric estimators (62F12) Inference from spatial processes (62M30) Random fields; image analysis (62M40) Markov processes: estimation; hidden Markov models (62M05)

Related Items

The Bayesian group Lasso for confounded spatial data, Penalty and related estimation strategies in the spatial error model, Bayesian nonstationary and nonparametric covariance estimation for large spatial data (with discussion), Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood, Variable selection for spatial Poisson point processes via a regularization method, Selection of spatial-temporal lattice models: assessing the impact of climate conditions on a mountain pine beetle outbreak, On estimation and selection of autologistic regression models via penalized pseudolikelihood, L₁ penalty and shrinkage estimation in partially linear models with random coefficient autoregressive errors, A skew Gaussian decomposable graphical model, Graph-Guided Banding of the Covariance Matrix

Uses Software

• symrcm
• spBayes
• ElemStatLearn

Cites Work

• Sparse inverse covariance estimation with the graphical lasso
• The Adaptive Lasso and Its Oracle Properties
• The NP-completeness of the bandwidth minimization problem
• Asymptotics for Lasso-type estimators.
• Least angle regression. (With discussion)
• Sparse permutation invariant covariance estimation
• Sparse estimation of large covariance matrices via a nested Lasso penalty
• High-dimensional graphs and variable selection with the Lasso
• Piecewise linear regularized solution paths
• Model selection and estimation in the Gaussian graphical model
• The bandwidth problem for graphs and matrices—a survey
• Computing the Minimum Fill-In is NP-Complete
• Estimation Methods for Models of Spatial Interaction
• Computing Dirichlet Tessellations in the Plane
• Fitting Gaussian Markov Random Fields to Gaussian Fields
• Estimation of nonstationary spatial covariance structure
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Adaptive Lasso for Cox's proportional hazards model
• Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
• Covariance matrix selection and estimation via penalised normal likelihood
• Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach.
• The elements of statistical learning. Data mining, inference, and prediction
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item