Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling

Article Properties
  • DOI (url)
    10.1214/19-ba1153
  • Publication Date
    2019/12/01
  • Journal
    Bayesian Analysis
  • Indian UGC (journal)
  • Refrences
    61
  • Citations
    2
  • Andrea Cremaschi
  • Raffaele Argiento
  • Katherine Shoemaker
  • Christine Peterson
  • Marina Vannucci
Refrences
Title Journal Journal Categories Citations Publication Date
Model selection and estimation in the Gaussian graphical model Biometrika
  • Science: Biology (General)
  • Medicine: Medicine (General): Computer applications to medicine. Medical informatics
  • Science: Biology (General)
  • Science: Mathematics: Probabilities. Mathematical statistics
  • Science: Mathematics
 819 2007
10.1214/12-EJS669
10.1214/12-BA729
Modeling Protein Expression and Protein Signaling Pathways Journal of the American Statistical Association
  • Science: Mathematics: Probabilities. Mathematical statistics
  • Science: Mathematics
 15 2012
10.1214/10-AOAS360
Citations
Title Journal Journal Categories Citations Publication Date
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data Journal of the American Statistical Association
  • Science: Mathematics: Probabilities. Mathematical statistics
  • Science: Mathematics
 1 2023
Learning Block Structured Graphs in Gaussian Graphical Models Journal of Computational and Graphical Statistics
  • Science: Mathematics: Probabilities. Mathematical statistics
  • Science: Mathematics
 2023
Citations Analysis
Category Category Repetition
Science: Mathematics: Probabilities. Mathematical statistics2
Science: Mathematics2
The category Science: Mathematics: Probabilities. Mathematical statistics 2 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data and was published in 2023. The most recent citation comes from a 2023 study titled Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data. This article reached its peak citation in 2023, with 2 citations. It has been cited in 2 different journals. Among related journals, the Journal of the American Statistical Association cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year