Statistics

This article examines how personalized medicine uses omics data to improve survival outcomes for patients.

This article discusses methods to handle missing data in longitudinal studies.

Exploring the importance of considering variations in treatment effects.

New statistical approaches improve understanding of treatment effects on health outcomes.

A new method enhances understanding of risk through past outcomes in studies.

This study analyzes commuting changes in Atlanta during COVID-19.

NBA2Vec changes how we analyze NBA player performance beyond traditional stats.

A look at how historical data informs clinical trial decisions.

RESI streamlines the reporting of effect sizes in research studies.

A focused approach to improving variational inference accuracy.

This study highlights methods for analyzing brain signals using covariance matrices.

Examining structures in data through connected clustering methods.

RNNC offers efficient analysis of satellite data, improving accuracy and speed.

A guide to spatio-temporal point processes and their applications across various fields.

CUSP priors improve Bayesian factor analysis by managing model complexity.

New sampling techniques enhance accuracy and efficiency in analyzing complex hidden systems.

A fresh approach helps identify discrimination in automated decision-making systems.

A look at data processing methods for improving predictive model outcomes.

This study enhances methods for sampling from complex distributions using KL divergence insights.

This study examines the Karcher mean's role in positive semi-definite matrices and distributed learning.

A focused approach to improving variational inference accuracy.

A new algorithm for estimating transformations amid data errors.

Improving conformal prediction using self-supervised learning for better uncertainty estimates.

A look into how Bayesian structure learning improves data modeling.

RESI streamlines the reporting of effect sizes in research studies.

A look at data processing methods for improving predictive model outcomes.

Strategies to control false discoveries enhance the reliability of scientific findings.

A focused approach to improving variational inference accuracy.

A deep dive into model confidence sets and variable selection in statistics.

Examining how logistic regression improves analysis of binary outcomes in various fields.

This study highlights methods for analyzing brain signals using covariance matrices.

This article discusses methods to handle missing data in longitudinal studies.

Examining the reliability and validation of influence functions in deep learning models.

A new algorithm enhances accuracy in forecasting disease outbreaks through detailed data generation.

A discussion on merging statistical approaches for better data analysis.

A fresh approach uncovers individual variations in program treatment effects.

Methods to accurately calculate averages and variances despite missing data points.

A method for predicting specific outcomes using relevant past data.

A new framework for better statistical inference in research and analysis.

A fresh approach improves signal detection in noisy environments with limited data.

A new algorithm for estimating transformations amid data errors.

A deep dive into model confidence sets and variable selection in statistics.

Techniques to identify important features in complex datasets are essential.

Integrating L2O transformations and graphical models improves parameter estimation in SEMs.

Lasso helps identify important data in complex datasets.

Analyzing relationships between groups of random variables for better insights.

This article discusses renewed interest in fiducial inference using generalized methods.

A study on how typhoid fever spreads using statistical modeling.