ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2017
Sparse graphs using exchangeable random measures.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Article
- Causal Inference for Social Network Data.Journal of the American Statistical Association · 2024Article
- Grand Canonical Ensembles of Sparse Networks and Bayesian Inference.Entropy (Basel, Switzerland) · 2022Article
- Hierarchical network models for exchangeable structured interaction processes.Journal of the American Statistical Association · 2022Article
- Inference of a universal social scale and segregation measures using social connectivity kernels.Journal of the Royal Society, Interface · 2020Article
- Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.Bayesian analysis · 2019Article
- Clinical Personal Connectomics Using Hybrid PET/MRI.Nuclear medicine and molecular imaging · 2019Article
- Multiresolution Network Models.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2019Article
- Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia.Biostatistics (Oxford, England) · 2018Article
- Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data.Entropy (Basel, Switzerland) · 2018Article
- EDGE EXCHANGEABLE MODELS FOR INTERACTION NETWORKS.Journal of the American Statistical Association · 2018Article
- On Edge Exchangeable Random Graphs.Journal of statistical physics · 2018Article
- Continuous representations of brain connectivity using spatial point processes.Medical image analysis · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this array-which can aid in modelling, computations and theoretical analysis-the Aldous-Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.