Latest Articles for Statistics

A clear overview of Multi-Channel Factor Analysis and its applications.

This article explores innovative sampling methods in neural networks using auxiliary variables.

Explore Mahonian numbers and their significance in counting permutations and inversions.

New algorithms improve efficiency and accuracy in counting problems.

Explore the significance of leverage scores in data analysis and privacy.

New models improve count data analysis for better decision-making.

A look into conditional independence and its role in statistical modeling.

This article explores the connection between slopes in functional regression models.

Examining the role and issues of instrumental variables in causal analysis.

A study on robust methods for analyzing complex data sets in neuroscience.

A study on identifying causal relationships amidst hidden variables and measurement errors.

This paper examines the behavior and challenges of mixture models in statistics.

A look at how models predict unseen data.

Learn the importance and methods of quantile estimation in various fields.

Learn how mathematical methods help us understand system behavior over time.

Learn about the hypergeometric distribution and its applications in statistics.

A look at how linear regression can help estimate causal effects in research.

Introducing a statistical model for better analysis of bounded data.

A look at how past events shape future occurrences in random processes.

Learn how interval varextropy helps measure uncertainty in specific data ranges.

This article discusses recovering probability distributions from noisy measurements using Chebyshev polynomials.

An overview of approximating Hilbert-Schmidt operators and their relevance in various fields.

This article analyzes time correlations in random growth, focusing on the inverse-gamma polymer model.

Exploring new statistical methods in quantum probability using operator on operator regression.

A look at using topological data analysis for better data classification.

An overview of Gaussian processes, kernels, and their significance in quantum theory and data analysis.

A unique perspective on the relationship between prime numbers and knots in mathematics.

A new approach to uncover causal relationships using pairwise analysis.

Learn how dimension reduction simplifies complex data for easier analysis and interpretation.

New methods improve dataset distillation for long-tailed datasets.

Optimal transport reshapes data science across multiple fields through efficient mass movement.

This study improves active learning using derivatives for better predictions.

Learn how concentration inequalities help analyze random matrices.

Examining the challenges in hypothesis testing for different statistical distributions.

A method to enhance how machine learning models handle new data.

A deep dive into methods for estimating banded and sparse covariance operators.

A new framework merges deep learning with semiparametric statistics for better data analysis.

Using matrix completion to improve time-series data accuracy.

Localized drift poses a challenge for predictive models in changing data streams.

Exploring the behavior and structure of random graphs in various contexts.