Directional tests in Gaussian graphical models
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Publication:6671921
DOI10.5705/SS.202022.0394MaRDI QIDQ6671921
Claudia Di Caterina, N. Reid, N. Sartori
Publication date: 27 January 2025
Published in: STATISTICA SINICA (Search for Journal in Brave)
saddlepoint approximationlikelihood ratio testexponential familyundirected graphcovariance selectionhigher-order asymptotics
Cites Work
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- On the use of saddlepoint approximations in high dimensional inference
- Directional testing for high dimensional multivariate normal distributions
- A review of Gaussian Markov models for conditional independence
- Likelihood asymptotics
- Graphical models. Methods for data analysis and mining
- Saddlepoint Approximations with Applications
- The Isserlis matrix and its application to non-decomposable graphical Gaussian models
- A simple general formula for tail probabilities for frequentist and Bayesian inference
- Accurate Directional Inference for Vector Parameters in Linear Exponential Families
- Modified Likelihood root in High Dimensions
- A directional look at F‐tests
- Power of edge exclusion tests in graphical Gaussian models
- Accurate directional inference for vector parameters
- Testing Statistical Hypotheses
- Graphical Models for Categorical Data
- Properties of sufficiency and statistical tests
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