Estimation of high-dimensional partially-observed discrete Markov random fields
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Publication:470504
DOI10.1214/14-EJS946zbMath1302.62206arXiv1108.2835WikidataQ105584274 ScholiaQ105584274MaRDI QIDQ470504
Publication date: 12 November 2014
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1108.2835
misspecificationpseudo-likelihoodMarkov random fieldshigh-dimensional inferencenetwork estimationpenalized likelihood inference
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- Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- Lasso-type recovery of sparse representations for high-dimensional data
- Sparsistency and rates of convergence in large covariance matrix estimation
- Gibbs measures and phase transitions
- Rejoinder: Latent variable graphical model selection via convex optimization
- Sparse permutation invariant covariance estimation
- Self-concordant analysis for logistic regression
- Simultaneous analysis of Lasso and Dantzig selector
- Regularized estimation of large covariance matrices
- High-dimensional graphs and variable selection with the Lasso
- Pairwise Variable Selection for High-Dimensional Model-Based Clustering
- Model selection for Gaussian concentration graphs
- Model selection and estimation in the Gaussian graphical model
- First-Order Methods for Sparse Covariance Selection
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
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