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Overall, the discussions predominantly focus on statistical methods and achievements, such as references to Grace Wahba’s work and Bayesian models, using technical language and acknowledgments of research milestones. This suggests a professional or academic audience engaged more with methodology than with the health implications of BMI trajectories.
The responses do not strongly support or oppose the article; some references to statistical methods hint at professionalism, but there's no clear consensus.
The posts show neutral interest, with some mentions of advanced statistical concepts but little focus on the specific health findings.
Discussions are somewhat superficial, mostly sharing links or brief comments without deep analysis of the study's implications.
The exchanges seem to have minimal impact on broader audiences, mainly involving niche academic references.
Grace Wahba, "the mother of smoothing splines," won the International Prize (aka the Nobel) in Statistics #stats for her representer theorem. youtu.be/8Ae_QzwwR_U?... So well deserved! I guess, one could also give her the price again in two years for her work on Generalized Cross-Validation.
The the Discussion Webinar "Latent Nested Nonparametric priors" is now available on the YouTube channel of the #BAjournal Watch it here: https://t.co/UPqMO8s1rr
@stephensenn Adjustment for a PS in a Bayesian nonparamateric outcome model (along with covariates) reduces regularization-induced confounding and restores valid inference relative to including the covariates alone. https://t.co/eQnG3MzpSO
BA Webinar: Latent Nested Nonparametric Priors” by Federico Camerlenghi - YouTube https://t.co/2DEKjFKqtA https://t.co/y5rC8ObDbY
RT @noah_greifer: @stephensenn Adjustment for a PS in a Bayesian nonparamateric outcome model (along with covariates) reduces regularizatio…
https://t.co/OIaFiQPj2w
This video discusses the groundbreaking ideas that led to the 2025 Nobel Prize in Statistics awarded to Grace Wahba. It covers her contributions and their impact on statistical sciences, highlighting innovative…
This episode explores the fundamentals of regression trees, focusing on Bayesian Additive Regression Trees. Sameer Deshpande, an expert in statistics, guides us through the methodology and applications of BARTs in a…
This episode explores the roots of regression trees and covers Bayesian Additive Regression Trees (BARTs). Hosted by Sameer Deshpande, an expert in Bayesian modeling, the discussion focuses on understanding BARTs'…
https://t.co/OIaFiQPj2w
Grace Wahba, "the mother of smoothing splines," won the International Prize (aka the Nobel) in Statistics #stats for her representer theorem. youtu.be/8Ae_QzwwR_U?... So well deserved! I guess, one could also give her the price again in two years for her work on Generalized Cross-Validation.
youtu.be/8Ae_QzwwR_U?...
RT @noah_greifer: @stephensenn Adjustment for a PS in a Bayesian nonparamateric outcome model (along with covariates) reduces regularizatio…
@stephensenn Adjustment for a PS in a Bayesian nonparamateric outcome model (along with covariates) reduces regularization-induced confounding and restores valid inference relative to including the covariates alone. https://t.co/eQnG3MzpSO
The the Discussion Webinar "Latent Nested Nonparametric priors" is now available on the YouTube channel of the #BAjournal Watch it here: https://t.co/UPqMO8s1rr
BA Webinar: Latent Nested Nonparametric Priors” by Federico Camerlenghi - YouTube https://t.co/2DEKjFKqtA https://t.co/y5rC8ObDbY
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