Statistics

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.

New methods enhance variable selection for better predictive accuracy in models.

Exploring how Health Wellness Coaching affects heart health and mental well-being.

New models improve prediction of extreme weather effects on ocean conditions.

Using machine learning to enhance diagnosis of appendicitis in children through ultrasound images.

Multi-view clustering improves patient classification and treatment customization in medical research.

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.

This article explores the Delaunay-Rips complex for analyzing complex data in machine learning.

EZtune streamlines hyperparameter tuning for machine learning models, aiding accuracy.

Research on faster attention computations enhances language modeling efficiency.

Lasso helps identify important data in complex datasets.

A look into the methods and theories behind set-to-set matching.

AI systems in job recommendations must be fair and unbiased.

Learn how calibration enhances treatment effect predictions in healthcare.

Combining U-statistics with importance-weighted variational inference enhances estimation reliability.

Research advances in generating synthetic images while safeguarding personal data privacy.

This paper discusses Natural Gradient Hybrid Variational Inference for complex models.

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

Researchers are merging studies to better tailor treatments for individuals.

A look at how historical data informs clinical trial decisions.

Self-normalization improves analysis of dependent scores in statistical models.

New methods enhance variable selection for better predictive accuracy in models.

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

Analyzing relationships between groups of random variables for better insights.

Introducing a neural network approach for better water level predictions.

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.

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.

Discover the properties and applications of regular simplex tensors in optimization.

Learn how negative controls enhance reliability in hypothesis testing.