The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks¶
Claim · All claims
Evidence · 1 study · designr2·q1
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2025: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? The estimation method often used with symptom networks, the frequentist graphical LASSO, has difficulty estimating centrality indices and uncertainty in these measures. → Joran Jongerling 2022
Evidence¶
Joran Jongerling 2022¶
Joran Jongerling, Sacha Epskamp, Donald Williams. (2022). Bayesian uncertainty estimation for Gaussian graphical models and centrality indices. Multivariate Behavioral Research. https://www.nwea.org/research/publication/bayesian-uncertainty-estimation-for-gaussian-graphical-models-and-centrality-indices/
q1 · i? · design · r2
The article's motivating statement about the frequentist GLASSO: it "has difficulty estimating (uncertainty in) these measures" — centrality indices capturing direct and indirect influences among symptoms. No supporting statistics are printed in the available text.
"the estimation method often used with these networks, the frequentist graphical LASSO (GLASSO), has difficulty estimating (uncertainty in) these measures."
Discussion¶
Related Claims¶
- In simulations, the Bayesian GLASSO performed better than the Horseshoe prior for estimating symptom networks — related
- Bayesian GLASSO shows good coverage of uncertainty for strength and closeness centrality, but uncertainty in betweenness centrality is estimated less well — related
- In simulations, Bayesian GLASSO estimation outperforms frequentist GLASSO on bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity — related
- Regularization-based graphical modeling has key drawbacks, including difficulty obtaining valid parameter uncertainty and inflated false positive rates — a broader claim this one bears on
- Changes in ICT career aspirations are not systematically linked to contemporaneous changes in other occupational domains — related
- Sensitivity is better for the frequentist GLASSO than for the Bayesian GLASSO, though Bayesian GLASSO performance is usually close — related
- Non-regularized graphical model methods are specifically designed for low-dimensional data common in the social-behavioral sciences — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
frequentist-glasso-difficulty-uncertainty-centrality |
title |
The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks |
description |
The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks |
status |
review |
evidence_strength |
weak |
newest_evidence |
2022 |
recheck_by |
2025 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — joran-jongerling-2022 |
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