Model Selection and the Principle of Minimum Description Length
From MaRDI portal
Publication:4419458
DOI10.1198/016214501753168398zbMath1017.62004OpenAlexW1973217014WikidataQ56061199 ScholiaQ56061199MaRDI QIDQ4419458
Publication date: 13 August 2003
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1198/016214501753168398
model selectiontime seriescluster analysisinformation theoryregressionAICBayesian methodsminimax lower boundscode lengthBayes information criterioncoding redundancypointwise lower bounds
Foundations and philosophical topics in statistics (62A01) Statistical aspects of information-theoretic topics (62B10)
Related Items
The polyharmonic local sine transform: a new tool for local image analysis and synthesis without edge effect, Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients, Description length and dimensionality reduction in functional data analysis, On the quantification of model uncertainty: a Bayesian perspective, A new approach of subgroup identification for high-dimensional longitudinal data, Information optimality and Bayesian modelling, Conditional sparse boosting for high-dimensional instrumental variable estimation, Prior distributions for objective Bayesian analysis, Wavelet-based gradient boosting, Scale-based Gaussian coverings: combining intra and inter mixture models in image segmentation, Model selection criteria for a linear model to solve discrete ill-posed problems on the basis of singular decomposition and random projection, APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001, An Introduction to Coding Theory and the Two-Part Minimum Description Length Principle, Prequential omnibus goodness-of-fit tests for stochastic processes: A numerical study, On the prevalence of information inconsistency in normal linear models, Rival approaches to mathematical modelling in immunology, On the selection of predictors by using greedy algorithms and information theoretic criteria, Unsupervised discretization by two-dimensional MDL-based histogram, SMLSOM: the shrinking maximum likelihood self-organizing map, Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling, Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy, Structure detection and parameter estimation for NARX models in a unified EM framework, MDL Mean Function Selection in Semiparametric Kernel Regression Models, Boosting algorithms: regularization, prediction and model fitting, Generalized Levinson--Durbin and Burg algorithms., Distance-based approach in univariate longitudinal data analysis, Early stopping in \(L_{2}\)Boosting, Hierarchical two-part MDL code for multinomial distributions, Catching up Faster by Switching Sooner: A Predictive Approach to Adaptive Estimation with an Application to the AIC–BIC Dilemma, Break Detection for a Class of Nonlinear Time Series Models, An Information-Geometric Approach to Learning Bayesian Network Topologies from Data, Efficient semiparametric estimation and model selection for multidimensional mixtures, Fitting of mixtures with unspecified number of components using cross validation distance estimate, Model uncertainty, Constructing a speculative kernel machine for pattern classification, A randomized method for solving discrete ill-posed problems, Variable selection in linear regression: several approaches based on normalized maximum likelihood, Model selection by sequentially normalized least squares, An MDL approach to the climate segmentation problem, Nonparametric Regression Based Image Analysis, Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach, Feature selection for Bayesian network classifiers using the MDL-FS score, Accumulative prediction error and the selection of time series models, Model selection by normalized maximum likelihood, Flexible scan statistic test to detect disease clusters in hierarchical trees, A minimum description length approach to hidden Markov models with Poisson and Gaussian emissions. Application to order identification, Selecting nonlinear time series models using information criteria, Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory, Structural changes estimation for strongly dependent processes, Minimum message length shrinkage estimation, Knot selection by boosting techniques, Choosing a Model Selection Strategy, Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle, Multiple changepoint detection with partial information on changepoint times, On the computation of entropy prior complexity and marginal prior distribution for the Bernoulli model, Boosting as a kernel-based method, Mixtures ofg-Priors in Generalized Linear Models, General Sparse Boosting: Improving Feature Selection of L2Boosting by Correlation-Based Penalty Family, Posterior model consistency in variable selection as the model dimension grows