Explainer: Did the Paris Climate Agreement work?

rss.org.uk

projecteuclid.org

Climate explainer: when and how should climate data be adjusted over time

rss.org.uk

Comparing variable selection and model averaging methods for logistic regression | PNAS

Model uncertainty is a central challenge in statistical models for binary outcomes such as logistic regression, arising when it is unclear which pr...

pnas.org

Pattern scaling: How local warming is related to global warming: An explainer

Abstract. In the fourth of an occasional series of explainers on the statistics and data underpinning our understanding of climate change, Adrian E. Rafter

academic.oup.com

Did the Paris Climate Agreement Work?

The Paris Climate Agreement was agreed by most of the world’s nations in 2015. It aimed to keep global average temperature increase over pre-industrial levels by 2100 to well below 2C. Using probabi...

communities.springernature.com

Emissions from economic growth undermine international progress on climate change, University of Washington study says

A study led by the University of Washington found that while the decade-old Paris Agreement has made some progress in fighting climate change, those gains have been outweighed by emissions associated ...

opb.org

Mitigation efforts to reduce carbon dioxide emissions and meet the Paris Agreement have been offset by economic growth - Communications Earth & Environment

Global carbon dioxide intensity declined from 2015 to 2024 following the Paris Agreement, but total emissions still increased due to economic growth, according to a global analysis of population, gros...

nature.com

Optimal pandemic control strategies and cost-effectiveness of COVID-19 non-pharmaceutical interventions in the United States - BMC Global and Public Health

Background Non-pharmaceutical interventions (NPIs) in response to the COVID-19 pandemic necessitated a trade-off between the health impacts of viral spread and the social and economic costs of restric...

bmcglobalpublichealth.biomedcentral.com

Home - BSS-ISBA

bss-isba.github.io

Home - BSS-ISBA

bss-isba.github.io

Optimal pandemic control strategies and cost-effectiveness of COVID-19 non-pharmaceutical interventions in the United States - BMC Global and Public Health

Background Non-pharmaceutical interventions (NPIs) in response to the COVID-19 pandemic necessitated a trade-off between the health impacts of viral spread and the social and economic costs of restric...

bmcglobalpublichealth.biomedcentral.com

Multiple imputation of hierarchical nonlinear time series data with an application to school enrollment data

International comparisons of hierarchical time series data sets based on survey data, such as annual country-level estimates of school enrollment rates, can suffer from large amounts of missing data due to differing coverage of surveys across countries and across times. A popular approach to handling missing data in these settings is through multiple imputation, which can be especially effective when there is an auxiliary variable that is strongly predictive of and has a smaller amount of missing data than the variable of interest. However, standard methods for multiple imputation of hierarchical time series data can perform poorly when the auxiliary variable and the variable of interest have a nonlinear relationship. Performance can also suffer if the multiple imputations are used to estimate an analysis model that makes different assumptions about the data compared to the imputation model, leading to uncongeniality between analysis and imputation models. We propose a Bayesian method for multiple imputation of hierarchical nonlinear time series data that uses a sequential decomposition of the joint distribution and incorporates smoothing splines to account for nonlinear relationships between variables. We compare the proposed method with existing multiple imputation methods through a simulation study and an application to secondary school enrollment data. We find that the proposed method can lead to substantial performance increases for estimation of parameters in uncongenial analysis models and for prediction of individual missing values.

projecteuclid.org

Adrian E. Raftery

sites.stat.washington.edu

Explainer: How local warming is related to global warming – pattern scaling

rss.org.uk

BayesPop

Probabilistic Population Projections

bayespop.csss.washington.edu

Sign The Open Letter to Stand Up For Science Now!

Science is under siege. Trump’s latest Executive Order calls for politically appointed science commissars to evaluate research. Join us in adding your name to our open letter condemning Trump’s escala...

actionnetwork.org

A privacy-preserved and high-utility synthesis strategy for risk-based stratified subgroups of the Canadian scleroderma patient registry data

Responsible data sharing anchors research reproducibility and promotes the integrity of scientific research. Motivated by Canadian Scleroderma Research Group (CSRG) patient registry data, we present a risk-based method to produce privacy-preserved and high-utility synthetic datasets, which also simultaneously imputes missing data of mixed continuous and categorical types in the original dataset. This method divides all individuals into different subgroups, based on their reidentification risks, and provides tailored synthesis strategies targeted for each risk subgroup, through the associated tuning mechanisms. Under our setting, our risk-based method reduced the number of patients at risk from 198 to four, among the 691 CSRG patients who have no missing values in any of the quasi-identifying variables, while preserving all correct inferential conclusions in the target analysis. The 95% confidence intervals (CIs) have 92.6% overlap, on average, with the CIs constructed using the unperturbed imputation-completed datasets. These findings suggest that our risk-based method makes it possible to release complete synthetic datasets for research reproducibility while ensuring that the reidentification risks are acceptably low. In contrast, the existing one-size-fits-all synthesis strategies that do not take account of different risk levels can lead to unnecessary information loss and possibly incorrect scientific conclusions.

projecteuclid.org

sites.stat.washington.edu