Introduction to Graphical Modelling
From MaRDI portal
Publication:4496859
DOI10.1007/978-1-4612-0493-0zbMath0952.62003OpenAlexW1494413412MaRDI QIDQ4496859
Publication date: 21 August 2000
Published in: Springer Texts in Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-1-4612-0493-0
model selectionchain graphsloglinear modelsindependence graphsgraphical Gaussian modelsWindowsgraphical modelling systemMIM 3.1
Nonparametric hypothesis testing (62G10) Generalized linear models (logistic models) (62J12) Linear inference, regression (62J99) Applications of graph theory (05C90) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Graphical methods in statistics (62A09)
Related Items
Estimation of nonparanormal graphical models based on ranked set sampling (RSS), Assessment of Covariance Selection Methods in High-Dimensional Gaussian Graphical Models, Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling Approach, Directed graphs and variable selection in large vector autoregressive models, Analysis of air quality time series of Hong Kong with graphical modeling, Generating knockoffs via conditional independence, Structure Learning of Contextual Markov Networks using Marginal Pseudo‐likelihood, A focused information criterion for graphical models, A skew Gaussian decomposable graphical model, A Unified Approach to the Characterization of Equivalence Classes of DAGs, Chain Graphs with no Flags and Chain Graphs, \textit{Graph\_sampler}: a simple tool for fully Bayesian analyses of DAG-models, A Multiple-Record Systems Estimation Method that Takes Observed and Unobserved Heterogeneity into Account, Bayesian propagation of record linkage uncertainty into population size estimation of human rights violations, Identifiability of Gaussian linear structural equation models with homogeneous and heterogeneous error variances, Detection of hubs in complex networks by the Laplacian matrix, Bayesian Network Structure Learning with Permutation Tests, HIGH-ORDER CONDITIONAL DISTANCE COVARIANCE WITH CONDITIONAL MUTUAL INDEPENDENCE, Identification of consistent functional genetic modules, Palindromic Bernoulli distributions, Dynamic path analysis -- a new approach to analyzing time-dependent covariates, Partial inversion for linear systems and partial closure of independence graphs, Joint Response Graphs and Separation Induced by Triangular Systems, A Characterization of the Log-Density Smoothing Spline ANOVA Model, Graphical chain models for the analysis of complex genetic diseases: an application to hypertension, Recent developments in high dimensional covariance estimation and its related issues, a review, On the impact of contaminations in graphical Gaussian models, Modelling discrete longitudinal data using acyclic probabilistic finite automata, Faithfulness and learning hypergraphs from discrete distributions, Structured learning of time-varying networks with application to PM2.5 data, Robust sparse Gaussian graphical modeling, A computational algebraic-geometry method for conditional-independence inference, Conditional independence graph for nonlinear time series and its application to international financial markets, Determining full conditional independence by low-order conditioning, A mathematical view of weights-of-evidence, conditional independence, and logistic regression in terms of Markov random fields, Structure learning of sparse directed acyclic graphs incorporating the scale-free property, Bayesian analysis of nonparanormal graphical models using rank-likelihood, Sequences of regressions and their independences, An inexact interior point method for \(L_{1}\)-regularized sparse covariance selection, Testing diagonality of high-dimensional covariance matrix under non-normality, Wishart distributions for decomposable covariance graph models, Reference priors for linear models with general covariance structures, Graphical modelling of multivariate time series, The log-linear group-lasso estimator and its asymptotic properties, Sparse directed acyclic graphs incorporating the covariates, Multiple testing and error control in Gaussian graphical model selection, Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection, AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms, The uncertainty of a selected graphical model, Relaxing the assumptions of knockoffs by conditioning, Reconstruction of a directed acyclic graph with intervention, Robust concentration graph model selection, Detection of information flow in major international financial markets by interactivity network analysis, Local structure recovery of chain graphs after marginalization, A note on the Lasso for Gaussian graphical model selection, Binary Models for Marginal Independence, Gaussian Markov random field spatial models in GAMLSS, Parameterizations and Fitting of Bi‐directed Graph Models to Categorical Data, Learning Oncogenic Pathways from Binary Genomic Instability Data, Conditional independence between two variables given any conditioning subset implies block diagonal covariance matrix for multivariate Gaussian distributions, Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model, A note on faithfulness and total positivity, An estimation of distribution algorithm for nurse scheduling, A SINful approach to Gaussian graphical model selection, Technological modelling for graphical models: an approach based on genetic algorithms, Tests for Gaussian graphical models, Empirical comparison study of approximate methods for structure selection in binary graphical models, Graphical Models with R. S.Højsgaard, D.Edwards, and S.Lauritzen (2012). Berlin: Springer. 182 pages, ISBN: 978‐1‐4614‐2298‐3., Data-driven kriging models based on FANOVA-decomposition, Review on statistical methods for gene network reconstruction using expression data, A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models, Adaptive estimation of stationary Gaussian fields, Graphical Models for Composable Finite Markov Processes, Network exploration via the adaptive LASSO and SCAD penalties, Collapsibility of Graphical CG-Regression Models, Unnamed Item, Robust Gaussian graphical modeling, A nonparametric test for block-diagonal covariance structure in high dimension and small samples, High-dimensional graphs and variable selection with the Lasso, Power of edge exclusion tests for graphical log-linear models, Estimation of high-dimensional graphical models using regularized score matching, Bayesian inference for graphical factor analysis models, Bayes admissible estimation of the means in Poisson decomposable graphical models, Decomposition of two classes of structural models, An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation, A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models, Log-multiplicative association models as item response models, Regularized rank-based estimation of high-dimensional nonparanormal graphical models, On the inclusion of bivariate marked point processes in graphical models, Effectiveness of combinations of Gaussian graphical models for model building, Graphical Models for Marked Point Processes Based on Local Independence, Context-specific graphical models for discrete longitudinal data, A computationally fast alternative to cross-validation in penalized Gaussian graphical models, Labelled Graphical Models, Atmospheric $$\hbox {CO}_2$$ and Global Temperatures: The Strength and Nature of Their Dependence, Bayesian model determination for multivariate ordinal and binary data, MIM, A refinement of the common cause principle, Gaussian covariance faithful Markov trees, Loss function, unbiasedness, and optimality of Gaussian graphical model selection, Nonparametric Bayes inference for concave distribution functions, Unnamed Item, On Block Ordering of Variables in Graphical Modelling, Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property, Testing coefficients of AR and bilinear time series models by a graphical approach, Unnamed Item, Selecting the tuning parameter in penalized Gaussian graphical models, Model-based clustering with sparse covariance matrices, Implications of faithfulness in graphical models, A Bartlett-type correction for likelihood ratio tests with application to testing equality of Gaussian graphical models, Inferring gene-gene interactions and functional modules using sparse canonical correlation analysis
Uses Software