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.

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 to managing investment risk by focusing on pessimistic outcomes.

A statistical approach to manage two related proportions effectively.

Researchers examine new methods to analyze survival data using machine learning techniques.

VaLLA enhances predictions by providing reliable uncertainty estimates in deep neural networks.

Examining the intersection of differential privacy and optimization challenges.

Techniques to identify important features in complex datasets are essential.

A new method for managing systems with delays and irregular data collection.

SCS helps identify which patients benefit from treatments, improving care.

Learn about UST, a method for comparing data with different total masses on graphs.

Research on faster attention computations enhances language modeling efficiency.

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.

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.

Examining structures in data through connected clustering methods.

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 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.

Exploring methods for analyzing differences in data distributions using E-variables.