Statistics

This study explores how teachers influence each other's beliefs and practices.

New methods enhance prediction accuracy and fairness in healthcare outcomes.

Analyzing the link between pollution and rising temperatures for effective policy action.

This article examines how access to healthy food impacts health outcomes in communities.

Researchers develop a fast emulator for complex computer models.

Exploring the impact of air pollution on mortality rates, especially among the elderly.

A new method enhances Alzheimer's risk prediction using electronic health records and advanced models.

Study develops automated methods to enhance accuracy in power grid load estimates.

A look into advanced techniques for better data assimilation and filtering.

Learn how lifted samplers improve sampling efficiency in MCMC methods.

A new algorithm enhances the analysis of calcium imaging data in real-time.

A new approach in federated learning captures data dependencies while ensuring privacy.

A new method estimates parameters of neuron activity using real data.

We develop methods for refining models using noisy and indirect observations.

This work improves sampling methods for complex systems stuck in certain states.

GMR offers a fresh approach to studying multiple factors and outcomes in research.

A new method improves image generation speed and quality.

A specialized machine learning model improves particle data analysis efficiency and accuracy.

A look at regularized algorithms and their impact on machine learning performance.

Learn how bagging boosts model performance across various applications.

A new method improves data learning by maintaining prior information.

Examining the importance of smallest eigenvalue in NTK for neural network training.

A new method improves efficiency in adapting to changing optimization problems.

Research reveals complexities in deep neural networks beyond traditional models.

A new method combines CNFs and MCMC for improved sampling from complex distributions.

New methods enhance prediction accuracy and fairness in healthcare outcomes.

Examining how interference affects treatment outcomes and improving estimation methods.

New methods for identifying significant changes in data variance over time.

This paper presents an efficient approach for real-time change detection in data.

A method for comparing samples with missing data helps improve research accuracy.

A look at causal machine learning methods and their impact in complex studies.

Introducing a framework to better assess common factor models using generalized residuals.

A look at how covariate adjustment clarifies research findings by managing confounding variables.

A method allowing adaptive statistical analysis without rigid model assumptions.

A new approach to boxplots ensures data privacy while maintaining insightful analysis.

This research develops methods for fair resource allocation despite limited user feedback.

A look into how the estimand framework improves clinical trial analysis.

A fresh approach to understanding complex human connections through fuzzy social network analysis.

A study on how Park-and-Ride can reduce congestion and emissions.

Information significantly impacts decision-making processes across various fields.

Learn how lifted samplers improve sampling efficiency in MCMC methods.

A look at methods for estimating extreme values in bivariate normal data.

A look at how to handle errors in score-based generative models.

A look at how calibration impacts model predictions and reliability.

CASBAH improves the study of treatment effects using continuous post-treatment variables.

A novel approach to measure dependencies in extreme data sets across various fields.

Learn how element-free distributions streamline analysis and improve data organization.

A new method enhances covariance estimation in Gaussian processes with adaptive thresholding.