Nonconcave penalized composite conditional likelihood estimation of sparse Ising models

DOI10.1214/12-AOS1017zbMath1284.62451arXiv1208.3555OpenAlexW3103506888MaRDI QIDQ693730

Tianxi Cai, Lingzhou Xue, Hui Zou

Publication date: 10 December 2012

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1208.3555

zbMATH Keywords

Ising modelcomposite likelihoodcoordinatewise optimizationHIV drug resistance databaseminorization-maximization principleNP-dimension asymptotic theory

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Estimating finite mixtures of ordinal graphical models, Unnamed Item, Aspects of likelihood inference, Multivariate Bernoulli distribution, Structure learning of exponential family graphical model with false discovery rate control, Fast Construction of Optimal Composite Likelihoods, Joint estimation of parameters in Ising model, An Additive Graphical Model for Discrete Data, Estimation of high-dimensional partially-observed discrete Markov random fields, On estimation and selection of autologistic regression models via penalized pseudolikelihood, Independent feature screening for ultrahigh-dimensional models with interactions, Bayesian model selection for high-dimensional Ising models, with applications to educational data, ROCKET: robust confidence intervals via Kendall's tau for transelliptical graphical models, Nonconcave penalized composite conditional likelihood estimation of sparse Ising models, Strong oracle optimality of folded concave penalized estimation, Model-based clustering of time-evolving networks through temporal exponential-family random graph models, Estimating heterogeneous graphical models for discrete data with an application to roll call voting, Unnamed Item, Unnamed Item, Unnamed Item

Uses Software

• glmnet

Cites Work

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