Mixture of Regression Models for Large Spatial Datasets
From MaRDI portal
Publication:6621664
DOI10.1080/00401706.2019.1569558MaRDI QIDQ6621664
Publication date: 18 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Adaptive Lasso and Its Oracle Properties
- \(\ell_{1}\)-penalization for mixture regression models
- Quantile regression in partially linear varying coefficient models
- Interquantile shrinkage and variable selection in quantile regression
- Exploring the sources of uncertainty: why does bagging for time series forecasting work?
- EM procedures using mean field-like approximations for Markov model-based image segmentation
- Finite mixture and Markov switching models.
- Multiresolution models for nonstationary spatial covariance functions
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- The mean field theory in EM procedures for Markov random fields
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Spatial Modeling With Spatially Varying Coefficient Processes
- Spectral methods for nonstationary spatial processes
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
- A Limited Memory Algorithm for Bound Constrained Optimization
- Spatial regression and estimation of disease risks: A clustering‐based approach
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
- Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes
- Bayesian unmasking in linear models.
- Regression‐based covariance functions for nonstationary spatial modeling
- Hidden Gibbs random fields model selection using block likelihood information criterion
This page was built for publication: Mixture of Regression Models for Large Spatial Datasets