New open access paper for anyone who uses categorical covariates (race, sex, group, etc.) in your linear models, especially with interactions to learn group-specific effects. Short version: the default approaches found in every statistical software and textbook have serious flaws and biases...[1/2]

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers

Categorical covariates such as race, sex, or group are ubiquitous in regression analysis. While main-only (or ANCOVA) linear models are predominant, linear models that include categorical-continuou...

doi.org

Thanks for the story @cornellcals.bsky.social! This work is about one of the simplest, most fundamental, and most widely-used statistical methods: OLS linear regression. We often use OLS linear regression to estimate heterogeneous (or subpopulation) effects. But... news.cornell.edu/stories/2026...

Statistics that tell the whole truth? It’s as easy as ABC | Cornell Chronicle

A Cornell statistics expert has come up with a method he believes can boost statistical power and significantly reduce bias – vital for research involving outcomes that differ by socioeconomics, race,...

news.cornell.edu

Jan 27 COPSS-NISS Webinar, The Role of Bayesian Statistics in an Age of AI, will feature David Dunson (Duke U.) and Xuming He (Washington U. in St. Louis) exploring Bayesian methods for transparency, interpretability, and decision-making in modern AI systems. Details 👇 www.niss.org/sites/defaul...

link 📈🤖 Efficient Bayesian inference for two-stage models in environmental epidemiology (Larin, Kowal) Statistical models often require inputs that are not completely known. This can occur when inputs are measured with error, indirectly, or when they are predicted using another model. In environm

Dear ISBA members, It is with great pleasure that we announce the completion of this year’s round of the Blackwell–Rosenbluth Award by j-ISBA, and that I have the honor of presenting our 2025 winners:

Uncovering Dynamic Relationships Between SARS-CoV-2 Wastewater Concentrations and Community Infections via Bayesian Spatial Functional Concurrent Regression @tandfresearch.bsky.social #COVID #DataScience www.tandfonline.com/doi/full/10....

New update to the SeBR package! Efficient, fully Bayesian inference for transformed linear/quantile/GP regressions. The learned transformation lets these models apply far more broadly: - Data that are skewed, heavy-tailed, multimodal... - Continuous real, positive, or [0,1] data

SeBR: Semiparametric Bayesian Regression Analysis

Monte Carlo sampling algorithms for semiparametric Bayesian regression analysis. These models feature a nonparametric (unknown) transformation of the data paired with widely-used regression models inc...

cran.r-project.org

Join the crowd of folks reading Brian's paper on count time series models!

New paper on probabilistic forecasting for multivariate time series! Work led by my former PhD student, John Zito:

A dynamic copula model for probabilistic forecasting of non-Gaussian multivariate time series

Multivariate time series (MTS) data often include a heterogeneous mix of non-Gaussian distributional features (asymmetry, multimodality, heavy tails) and data types (continuous and discrete variables)...

arxiv.org

Immensely sad that Herman has passed away. He took me into the Bayesian community at the Valencia meeting in 1998. Very encouraging discussant of my thesis. Always helping the younger and willing to share his time and knowledge. He will be missed by many. Obituary: esobe.org/Herman_van_D...

Since this work has gotten some attention, reposting for all the new folks here! www.nationaltribune.com.au/new-statisti...

New statistical method powers research on health, climate, financial data

Machine learning and artificial intelligence wouldn’t be possible without the statistical models that underpin their analytic capabilities. A Cornell

nationaltribune.com.au

Dan Kowal@dan-kowal.bsky.social · 2y ago

Since I'm new here, I'll link to my most recent paper (open access!). It's a blend of Bayesian regression, data transformations, and computing. #bayes #stats doi.org/10.1080/0162...

We're hiring! Positions available at the Asst/Assoc level. Wonderful collaborative opportunities with @cornellcals.bsky.social and throughout the university. And we're moving to a brand new building for data/computing/info sciences this summer! Please apply: academicjobsonline.org/ajo/jobs/28628

I've never had anything "trending" before...thanks @cornellcals.bsky.social and Phys.org for getting the word out! #Statistics #AcademicChatter #academicsky #Research #Science

Since I'm new here, I'll link to my most recent paper (open access!). It's a blend of Bayesian regression, data transformations, and computing. #bayes #stats doi.org/10.1080/0162...

Monte Carlo Inference for Semiparametric Bayesian Regression

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically invo...

doi.org