Latest Articles for Statistics

New algorithms improve performance and memory use in streaming data analysis.

A look at PCR's role in analyzing high-dimensional datasets.

A novel technique generates samples efficiently from unknown probability distributions using limited data.

A versatile tool for analyzing diverse data distributions in statistics.

This article explores strategies for improving model generalization and understanding gradient behavior.

Introducing a non-parametric Box-Cox model for better analysis of high-dimensional data.

A clear guide to the EM algorithm and its connection to probability distributions.

Explore the role of self-normalized partial sums in time series analysis.

Introducing a method to improve data analysis using hierarchical modeling and Bayesian statistics.

Study reveals random walks mimic Brownian motion in structured environments.

Introducing EBARS for optimal knot selection in multivariate spline regression.

This article discusses a new framework for kernel testing in complex data analysis.

A new method for analyzing spatio-temporal data across various fields.

Addressing missing data is vital for reliable healthcare research and patient care.

Effective methods to manage noisy labels in high-dimensional data for better classification.

Methods for estimating reliability in machine learning models.

New parameterization method improves state-space model performance in various applications.

A new method improves sampling from high-dimensional data using reinforcement learning.

A look into new methods for improving matrix denoising amidst complex noise.

Analyzing data privacy through Bayesian inference with constraints.

A new method enhances data analysis in cosmology using Bayesian inference.

A framework to enhance Gaussian Process Regression's predictions and uncertainty measures.

A look into how probability and causal reasoning shape our understanding and decisions.

New approaches enhance data sampling from complex distributions in statistics and machine learning.

This paper proposes using reinforcement learning to enhance adaptive MCMC algorithms.

Addressing overfitting and privacy issues in evolving datasets.

A new approach to optimize Bayesian inference using generalized methods.

Discover a novel approach to causal inference with cocycles.

A look at how conformal prediction aids in making confident decisions.

Explore the directed landscape and its implications in probability and statistics.

Explore the significance of random matrices in various fields and their eigenvalue behavior.

Learn how matrix sketching simplifies large datasets.

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

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 calibration impacts model predictions and reliability.

Exploring the significance of random matrices in various fields and their eigenvalues.

Study key concepts in probability and geometry that shape random structures.

Explore strong screening rules for fast feature selection in complex datasets.

AI tools are reshaping how we interpret statistical testing and its implications.