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High-dimensional Inference and FDR Control for Simulated Markov Random Fields

arXiv 2202.05612 ↗First posted 11 Feb 2022stat.ML cs.LG math.STcited by 0

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Abstract (arXiv)

Identifying important features linked to a response variable is a fundamental task in various scientific domains. This article explores statistical inference for simulated Markov random fields in high-dimensional settings. We introduce a methodology based on Markov Chain Monte Carlo Maximum Likelihood Estimation (MCMC-MLE) with Elastic-net regularization. Under mild conditions on the MCMC method, our penalized MCMC-MLE method achieves ℓ1-consistency. We propose a decorrelated score test, establishing both its asymptotic normality and that of a one-step estimator, along with the associated confidence interval. Furthermore, we construct two false discovery rate control procedures via the asymptotic behaviors for both p-values and e-values. Comprehensive numerical simulations confirm the theoretical validity of the proposed methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 72 references that are in the map. First posted on arXiv 11 Feb 2022.

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References · 14

  1. 2014Controlling the false discovery rate via knockoffscited by 97
  2. 2018Deep Knockoffscited by 41
  3. 2014A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Modelscited by 26
  4. 2020Concentration Inequalities for Statistical Inferencecited by 24
  5. 2020False Discovery Rate Control via Data Splittingcited by 12
  6. 2017Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detectioncited by 8
  7. 2020A Scale-free Approach for False Discovery Rate Control in Generalized Linear Modelscited by 8
  8. 2018Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Modelscited by 7
  9. 2014Testing and Confidence Intervals for High Dimensional Proportional Hazards Modelcited by 6
  10. 2020Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Errorcited by 4
  11. 2016Sparse estimation in Ising Model via penalized Monte Carlo methodscited by 2
  12. 2019Relaxing the Assumptions of Knockoffs by Conditioningcited by 2
2 more
  1. 2020False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregationcited by 2
  2. 2021Directional FDR Control for Sub-Gaussian Sparse GLMscited by 2

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