Extended Bayesian information criteria for model selection with large model spaces

From MaRDI portal
Publication:3181918

DOI10.1093/biomet/asn034zbMath1437.62415OpenAlexW2053061982WikidataQ57310183 ScholiaQ57310183MaRDI QIDQ3181918

Zehua Chen, Jiahua Chen

Publication date: 30 September 2009

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biomet/asn034



Related Items

A discussion of ‘prior-based Bayesian information criterion’, A discussion of prior-based Bayesian information criterion (PBIC), Discussion of prior-based Bayesian information criterion (PBIC) by M.J. Bayarria, James O. Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi, and Ingmar Visser, Bayesian variable selection via a benchmark in normal linear models, Sequential profile Lasso for ultra-high-dimensional partially linear models, Prioritizing Autism Risk Genes Using Personalized Graphical Models Estimated From Single-Cell RNA-seq Data, A general framework of online updating variable selection for generalized linear models with streaming datasets, Nonlinear Variable Selection via Deep Neural Networks, Least-Square Approximation for a Distributed System, Unnamed Item, High-dimensional Varying Index Coefficient Quantile Regression Model, Consistency of BIC Model Averaging, Bayesian generalized fused lasso modeling via NEG distribution, Estimating finite mixture of continuous trees using penalized mutual information, Exploiting Disagreement Between High-Dimensional Variable Selectors for Uncertainty Visualization, Clustering High-Dimensional Time Series Based on Parallelism, Bayesian Subset Modeling for High-Dimensional Generalized Linear Models, Smoothed quantile regression with nonignorable dropouts, Toward an objective and reproducible model choice via variable selection deviation, A Note on High-Dimensional Linear Regression With Interactions, Model Selection of Generalized Estimating Equation With Divergent Model Size, Bayesian Approaches to Shrinkage and Sparse Estimation, Polynomial network autoregressive models with divergent orders, Forward variable selection for ultra-high dimensional quantile regression models, Statistical quality control using image intelligence: A sparse learning approach, Functional Group Bridge for Simultaneous Regression and Support Estimation, Gene-environment interaction analysis under the Cox model, Improved composite quantile regression and variable selection with nonignorable dropouts, Joint learning of multiple Granger causal networks via non-convex regularizations: inference of group-level brain connectivity, Consistent Bayesian information criterion based on a mixture prior for possibly high‐dimensional multivariate linear regression models, On the selection of predictors by using greedy algorithms and information theoretic criteria, Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization, Joint Gene Network Construction by Single-Cell RNA Sequencing Data, Variable selection in nonlinear function‐on‐scalar regression, Path algorithms for fused lasso signal approximator with application to COVID‐19 spread in Korea, Penetrating sporadic return predictability, Quantile forward regression for high-dimensional survival data, Structured Ultrahigh Dimensional Multiple-Index Models with Efficient Estimation in Computation And Theory, A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease, A primal dual active set with continuation algorithm for high-dimensional nonconvex SICA-penalized regression, Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis, RaSE: A Variable Screening Framework via Random Subspace Ensembles, Robust feature screening procedures for single and mixed types of data, A modified information criterion for tuning parameter selection in 1d fused LASSO for inference on multiple change points, Neuronized Priors for Bayesian Sparse Linear Regression, Variable Selection in the Presence of Factors: A Model Selection Perspective, Integrative Factor Regression and Its Inference for Multimodal Data Analysis, Forward selection for feature screening and structure identification in varying coefficient models, A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data, Risk spillover network structure learning for correlated financial assets: a directed acyclic graph approach, Variable selection for nonparametric additive Cox model with interval‐censored data, Nonlinear directed acyclic graph estimation based on the kernel partial correlation coefficient, Latent Network Structure Learning From High-Dimensional Multivariate Point Processes, Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data, Learning latent and hierarchical structures in cognitive diagnosis models, Nonlinear Factor‐Augmented Predictive Regression Models with Functional Coefficients, Forward-selected panel data approach for program evaluation, Clustering multivariate count data via Dirichlet-multinomial network fusion, Empirical likelihood based tests for detecting the presence of significant predictors in marginal quantile regression, A semi-parametric approach to feature selection in high-dimensional linear regression models, The scalable birth-death MCMC algorithm for mixed graphical model learning with application to genomic data integration, Bayesian combinatorial multistudy factor analysis, Tuning parameter selection in fused lasso signal approximator with false discovery rate control, Local Whittle estimation of high-dimensional long-run variance and precision matrices, Culling the Herd of Moments with Penalized Empirical Likelihood, Covariance Model with General Linear Structure and Divergent Parameters, Feature Selection by Canonical Correlation Search in High-Dimensional Multiresponse Models With Complex Group Structures, Unnamed Item, Variable selection for multivariate generalized linear models, A Model-free Variable Screening Method Based on Leverage Score, Toward a Multisubject Analysis of Neural Connectivity, Unnamed Item, MARS as an alternative approach of Gaussian graphical model for biochemical networks, A Tuning-free Robust and Efficient Approach to High-dimensional Regression, Feature Screening for Network Autoregression Model, Constructing networks by filtering correlation matrices: a null model approach, A polynomial algorithm for best-subset selection problem, Robust Variable Selection With Exponential Squared Loss, A Robust Consistent Information Criterion for Model Selection Based on Empirical Likelihood, The EBIC and a sequential procedure for feature selection in interactive linear models with high-dimensional data, Likelihood adaptively modified penalties, Discussion on “Two-Stage Procedures for High-Dimensional Data” by Makoto Aoshima and Kazuyoshi Yata, Nested coordinate descent algorithms for empirical likelihood, A stepwise regression algorithm for high-dimensional variable selection, Unnamed Item, Robust Variable and Interaction Selection for Logistic Regression and General Index Models, Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets, Unnamed Item, A sequential scaled pairwise selection approach to edge detection in nonparanormal graphical models, Structural identification and variable selection in high-dimensional varying-coefficient models, Sparse sufficient dimension reduction using optimal scoring, A sure independence screening procedure for ultra-high dimensional partially linear additive models, Semi-Standard Partial Covariance Variable Selection When Irrepresentable Conditions Fail, Robust statistical inference for longitudinal data with nonignorable dropouts, A robust and efficient variable selection method for linear regression, Multiple Loci Mapping via Model-free Variable Selection, SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part, Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis, Smoothed partially linear quantile regression with nonignorable missing response, Novel model selection criteria for LMARS: MARS designed for biological networks, The joy of proofs in statistical research, Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes, Bayesian Neural Networks for Selection of Drug Sensitive Genes, Identifying Latent Structures in Restricted Latent Class Models, Variable selection and parameter estimation via WLAD-SCAD with a diverging number of parameters, Empirical likelihood based variable selection, ESL-SELO: a robust image denoising algorithm with penalty, Robust sparse Gaussian graphical modeling, The Sparse MLE for Ultrahigh-Dimensional Feature Screening, Multilevel Gaussian graphical model for multilevel networks, Bayesian selection of best subsets via hybrid search, Unnamed Item, Laplace Error Penalty-based Variable Selection in High Dimension, Empirical Likelihood for Censored Linear Regression and Variable Selection, Graph-based sparse linear discriminant analysis for high-dimensional classification, Joint estimation of heterogeneous exponential Markov random fields through an approximate likelihood inference, Consistent tuning parameter selection in high-dimensional group-penalized regression, Nearly optimal Bayesian shrinkage for high-dimensional regression, Unnamed Item, Baseline drift estimation for air quality data using quantile trend filtering, Identifying local differences with fused-MCP: an apartment rental market case study on geographical segmentation detection, A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification, Information criteria for latent factor models: a study on factor pervasiveness and adaptivity, Sequential feature screening for generalized linear models with sparse ultra-high dimensional data, Cluster feature selection in high-dimensional linear models, Ultra-high dimensional variable screening via Gram-Schmidt orthogonalization, Bayesian fusion estimation via \(t\) shrinkage, Calibrating nonconvex penalized regression in ultra-high dimension, Model selection for high-dimensional linear regression with dependent observations, Estimation and variable selection with exponential weights, Dynamic tilted current correlation for high dimensional variable screening, Sparse identification of truncation errors, Multiple predictingK-fold cross-validation for model selection, Integrative network learning for multimodality biomarker data, Dependence in elliptical partial correlation graphs, Feature selection in finite mixture of sparse normal linear models in high-dimensional feature space, Greedy forward regression for variable screening, Robust low-rank multiple kernel learning with compound regularization, Trace pursuit variable selection for multi-population data, Estimation of undirected graph with finite mixture of nonparanormal distribution, Bayesian model selection for high-dimensional Ising models, with applications to educational data, A sequential approach to feature selection in high-dimensional additive models, Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates, Likelihood-Based Selection and Sharp Parameter Estimation, Identifying QTLs and epistasis in structured plant populations using adaptive mixed LASSO, Data science, big data and statistics, Sparse model identification and learning for ultra-high-dimensional additive partially linear models, Forward regression for Cox models with high-dimensional covariates, Rank reduction for high-dimensional generalized additive models, Modified versions of the Bayesian information criterion for sparse generalized linear models, Forward-Backward Selection with Early Dropping, Smoothed empirical likelihood inference and variable selection for quantile regression with nonignorable missing response, PenPC : A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs, Adaptive group bridge selection in the semiparametric accelerated failure time model, Model Selection for High-Dimensional Quadratic Regression via Regularization, Application of model selection technique in chemogenomic data analysis, Model selection procedure for high‐dimensional data, Two tales of variable selection for high dimensional regression: Screening and model building, Sparse estimation of multivariate Poisson log‐normal models from count data, Robust Bayesian model selection for variable clustering with the Gaussian graphical model, A two-stage sequential conditional selection approach to sparse high-dimensional multivariate regression models, Particle swarm stepwise (PaSS) algorithm for information criteria-based variable selections, Coordinate majorization descent algorithm for nonconvex penalized regression, Model Selection via Bayesian Information Criterion for Quantile Regression Models, Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling, Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space, Interaction Screening for Ultrahigh-Dimensional Data, Global optimal model selection for high-dimensional survival analysis, A penalized estimation for the Cox model with ordinal multinomial covariates, Algorithms for Fitting the Constrained Lasso, Iterative Proportional Scaling Revisited: A Modern Optimization Perspective, Influence Diagnostics for High-Dimensional Lasso Regression, Diagonal Discriminant Analysis With Feature Selection for High-Dimensional Data, Variable selection for additive model via cumulative ratios of empirical strengths total, Selection Consistency of Generalized Information Criterion for Sparse Logistic Model, Penalized empirical likelihood for the sparse Cox regression model, Robust measurement via a fused latent and graphical item response theory model, A Sparse Learning Approach to Relative-Volatility-Managed Portfolio Selection, Order selection for possibly infinite-order non-stationary time series, Adaptively weighted group Lasso for semiparametric quantile regression models, Stochastic proximal-gradient algorithms for penalized mixed models, Selecting the tuning parameter in penalized Gaussian graphical models, Model-based clustering with sparse covariance matrices, On Cross-Validation for Sparse Reduced Rank Regression, High-dimensional Ising model selection with Bayesian information criteria, Sustainable Entrepreneurship on Thailand’s SMEs, Unnamed Item, SCAD-penalised generalised additive models with non-polynomial dimensionality, Variable selection in high-dimensional partly linear additive models, Extended Bayesian information criterion in the Cox model with a high-dimensional feature space, Variable selection for functional linear models with strong heredity constraint, Edge detection in sparse Gaussian graphical models, Single- and multiple-group penalized factor analysis: a trust-region algorithm approach with integrated automatic multiple tuning parameter selection, Using penalized EM algorithm to infer learning trajectories in latent transition CDM, Local linear smoothing for sparse high dimensional varying coefficient models, Forward variable selection for sparse ultra-high-dimensional generalized varying coefficient models, A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions, Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation, Partially linear structure identification in generalized additive models with NP-dimensionality, Testing conditional mean through regression model sequence using Yanai's generalized coefficient of determination, A scalable surrogate \(L_0\) sparse regression method for generalized linear models with applications to large scale data, Sparse vector heterogeneous autoregressive modeling for realized volatility, GSDAR: a fast Newton algorithm for \(\ell_0\) regularized generalized linear models with statistical guarantee, Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach, High-dimensional penalty selection via minimum description length principle, Penalized quasi-likelihood estimation of generalized Pareto regression -- consistent identification of risk factors for extreme losses, Variable selection in censored quantile regression with high dimensional data, Objective Bayesian edge screening and structure selection for Ising networks, Modeling latent topics in social media using dynamic exploratory graph analysis: the case of the right-wing and left-wing trolls in the 2016 US elections, Designing penalty functions in high dimensional problems: the role of tuning parameters, Random subspace method for high-dimensional regression with the \texttt{R} package \texttt{regRSM}, Least squares approximation with a diverging number of parameters, Detecting abrupt changes in the spectra of high-energy astrophysical sources, Penalised inference for lagged dependent regression in the presence of autocorrelated residuals, Bayesian joint inference for multiple directed acyclic graphs, High-dimensional \(A\)-learning for optimal dynamic treatment regimes, Variable selection and parameter estimation with the Atan regularization method, Integrative weighted group Lasso and generalized local quadratic approximation, On constrained and regularized high-dimensional regression, Fitting very large sparse Gaussian graphical models, An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors, Variable selection in high-dimensional quantile varying coefficient models, Semiparametric Bayesian information criterion for model selection in ultra-high dimensional additive models, Simultaneous confidence bands for sequential autoregressive fitting, Statistical properties of convex clustering, Regularized latent class analysis with application in cognitive diagnosis, Goodness-of-fit testing-based selection for large-\(p\)-small-\(n\) problems: a two-stage ranking approach, Estimation and inference in generalized additive coefficient models for nonlinear interactions with high-dimensional covariates, Globally adaptive quantile regression with ultra-high dimensional data, Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression, Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models, A selective overview of feature screening for ultrahigh-dimensional data, Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data, Rejoinder: ``Robust Bayesian graphical modeling using Dirichlet \(t\)-distributions, Generalized network psychometrics: combining network and latent variable models, Nonconvex penalized reduced rank regression and its oracle properties in high dimensions, Inferring large graphs using \(\ell_1\)-penalized likelihood, On efficient calculations for Bayesian variable selection, Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts, A note on the consistency of Schwarz's criterion in linear quantile regression with the SCAD penalty, High-dimensional Cox regression analysis in genetic studies with censored survival outcomes, Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis, A two-stage regularization method for variable selection and forecasting in high-order interaction model, A systematic review on model selection in high-dimensional regression, Regression with stagewise minimization on risk function, Fusion learning algorithm to combine partially heterogeneous Cox models, Penalized weighted composite quantile regression in the linear regression model with heavy-tailed autocorrelated errors, Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations, Exact post-selection inference for the generalized Lasso path, Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models, Smooth predictive model fitting in regression, A new scope of penalized empirical likelihood with high-dimensional estimating equations, High-dimensional consistency in score-based and hybrid structure learning, Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials, Spline estimator for ultra-high dimensional partially linear varying coefficient models, Model selection in sparse high-dimensional vine copula models with an application to portfolio risk, A Lasso-penalized BIC for mixture model selection, Delete or merge regressors for linear model selection, Consistent tuning parameter selection in high dimensional sparse linear regression, Ridge-forward quadratic discriminant analysis in high-dimensional situations, A distribution-based Lasso for a general single-index model, Variable selection for partially linear models via partial correlation, Variable selection in nonparametric additive models, Selection by partitioning the solution paths, A flexible shrinkage operator for fussy grouped variable selection, Simultaneous feature selection and clustering based on square root optimization, Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks, Estimation of high-dimensional graphical models using regularized score matching, Broken adaptive ridge regression and its asymptotic properties, Variable selection in high-dimensional partially linear additive models for composite quantile regression, Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts, High-dimensional variable selection via low-dimensional adaptive learning, The ranking lasso and its application to sport tournaments, Gini correlation for feature screening, Sparse estimation of Cox proportional hazards models via approximated information criteria, Variational approximation for heteroscedastic linear models and matching pursuit algorithms, APPLE: approximate path for penalized likelihood estimators, Spline estimator for simultaneous variable selection and constant coefficient identification in high-dimensional generalized varying-coefficient models, Sparse group fused Lasso for model segmentation: a hybrid approach, Least product relative error estimation for identification in multiplicative additive models, Broken adaptive ridge regression for right-censored survival data, VCSEL: prioritizing SNP-set by penalized variance component selection, Subgroup analysis for high-dimensional functional regression, Scalar on network regression via boosting, Tournament screening cum EBIC for feature selection with high-dimensional feature spaces, A sequential feature selection procedure for high-dimensional Cox proportional hazards model, Locally associated graphical models and mixed convex exponential families, Sparse spatio-temporal autoregressions by profiling and bagging, Sparse logistic functional principal component analysis for binary data, Sparse estimation via nonconcave penalized likelihood in factor analysis model