Latest Articles for Statistics

A look into eigenvalues, GUE matrices, and sampling methods for better calculation.

A new statistical model to analyze event occurrences over time.

An overview of Bayesian computation's growth and its integration with machine learning.

Learn about permutations and their significance in combinatorial statistics.

A look into techniques for analyzing categorical time series data.

Explore the significance and tools for analyzing ordinal time series data.

This article examines how resetting costs affect task efficiency.

Introducing distance measures for effective clustering of ordinal time series data.

Learn how Tensor PCA simplifies high-dimensional data analysis effectively.

Explore how depth functions enhance statistical analysis and understanding of data distributions.

Learn how spectral smoothing improves Andrews plots for complex data.

A new method enhances the accuracy of two-sample testing amidst hidden differences.

Explore single-pair matrices, their properties, and the calculation of their inverses.

Exploring MAP estimators in Bayesian analysis of inverse problems.

A look into how conditional DPPs impact random matrix theory and beyond.

GGLM tackles data dependencies for better insights in complex situations.

Learn how persistent homology helps analyze complex data structures.

A new method improves accuracy in calculating derivatives from noisy data.

Explore the role of ESI in analyzing random variables to improve predictive accuracy.

A method to analyze imperfect data without bias.

Explore the importance and implications of Bernoulli processes in statistics and machine learning.

A new method enhances the analysis of complex functional data.

Explore the unique characteristics and counting methods of cycle-alternating permutations.

Introducing a method for effective time series classification using the LLT technique.

A new approach enhances KSD tests to detect subtle differences in distributions.

This study links integrable systems to random matrix theory via a polynomial Central Limit Theorem.

New algorithm improves subdata selection methods for better analysis of large datasets.

This article discusses ways to speed up computations in additive Gaussian Processes.

A look into varentropy and its weighted forms for better uncertainty assessment.

Exploring how Bayesian inference enhances data interpretation in neuroscience research.

A new method, OBCut, improves clustering efficiency and accuracy for large datasets.

This method improves data analysis by addressing ties in correlation measurements.

Explore beta distribution's properties and methods for practical applications in various fields.

New methods improve the comparison of data distributions in machine learning.

A simple analogy for grasping quantum mechanics and computing principles.

A fresh look at multiverse analysis and its impact on scientific research.

This article presents a fresh approach for analyzing symmetry in multivariate data.

New robust estimators enhance functional data analysis despite outliers.

Examining the impact and management of bias in data-driven systems.

A new approach to improve decision-making under uncertainty using inferential moments.