ArticleBiometrika2015
Selection and estimation for mixed graphical models.
Article in Biometrika, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed.
- Temporal dynamics of psychosocial risk and resilience pathways in adolescent depressive and anxiety symptoms: a three-wave longitudinal study.BMC psychology · 2026Article
- The social-ecological model of depressive symptoms in middle aged in China: a network analysis.BMC public health · 2025Article
- Dietary habits and complex food relations in Northwest China: a population-based network analysis.BMJ nutrition, prevention & health · 2025Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Graphical Model Inference with Erosely Measured Data.Journal of the American Statistical Association · 2024Article
- THE SCALABLE BIRTH-DEATH MCMC ALGORITHM FOR MIXED GRAPHICAL MODEL LEARNING WITH APPLICATION TO GENOMIC DATA INTEGRATION.The annals of applied statistics · 2023Article
- Markov neighborhood regression for statistical inference of high-dimensional generalized linear models.Statistics in medicine · 2022Article
- Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks.Sankhya. Series A. (2008) · 2022Article
- Estimating Finite Mixtures of Ordinal Graphical Models.Psychometrika · 2022Article
- Nonparametric Neighborhood Selection in Graphical Models.Journal of machine learning research : JMLR · 2022Article
- Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data.Journal of the American Statistical Association · 2022Article
- Multi-omics integration in biomedical research - A metabolomics-centric review.Analytica chimica acta · 2021Review
- Recent Integrations of Latent Variable Network Modeling With Psychometric Models.Frontiers in psychology · 2021Review
- Compositional zero-inflated network estimation for microbiome data.BMC bioinformatics · 2020Article
- On specification tests for composite likelihood inference.Biometrika · 2020Article
- Nonparametric graphical model for counts.Journal of machine learning research : JMLR · 2020Article
- Unpacking Associations between Mood Symptoms and Screen Time in Preadolescents: a Network Analysis.Journal of abnormal child psychology · 2020Article
- A Projection-based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models.Journal of econometrics · 2020Article
- Bayesian inference of networks across multiple sample groups and data types.Biostatistics (Oxford, England) · 2020Article
- Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, we investigate the selection of edges between nodes whose conditional distributions take different parametric forms, and show that efficiency can be gained if edge estimates obtained from the regressions of particular nodes are used to reconstruct the graph. These results are illustrated with examples of Gaussian, Bernoulli, Poisson and exponential distributions. Our theoretical findings are corroborated by evidence from simulation studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.