Latest Articles for Statistics

New methods improve covariance estimation amid data challenges.

Learn how triclustering helps uncover patterns in complex tensor data.

A new hybrid method improves mutual information estimation by combining generative and discriminative techniques.

A look into masked pre-training and its connection to model performance.

Research examines Tsallis statistics for particle behavior in high-energy collisions.

Insights on convergence rates and distribution behaviors in statistics.

Exploring the impact and methods of mortality data analysis.

This article examines how martingales model probability shifts in sports events.

This article simplifies Fisher Geometry and its importance for understanding statistical relationships.

Learn about efficient methods for tackling complex saddle-point problems.

New Stirling-gamma distribution enhances flexibility in data clustering.

Exploring methods to extract signals from noisy datasets across various fields.

Exploring eigenvalues in specific Toeplitz matrices with two non-zero off-diagonals.

Learn how Gaussian random fields help correct the look-elsewhere effect in physics data analysis.

A new method enhances efficiency in analyzing latent Gaussian models.

A look into functional dependencies and their significance in data accuracy.

A new optimization method improves speed and efficiency in large data scenarios.

This article examines how scientists analyze cosmic data and the role of covariance.

New method enhances parameter estimation flexibility in partially linear models.

Study of rectangle packing efficiency using random sequential adsorption method.

Learn how sketching algorithms simplify large data set analysis.

A new method improves speed and accuracy in U-statistics risk control.

This paper presents improved techniques for analyzing cointegrated time series relationships.

A new method for measuring discretization errors using Wishart distribution.

Learn how sampling techniques shape research and data analysis.

Explore the strong B-inequality and its significance in mathematics.

Examining multivariate Dickman distributions and Vervaat perpetuities for enhanced data analysis.

A deep dive into optimal transport methods and their applications.

Improving the evaluation of Bayesian models through efficient calibration techniques.

CSSLR enhances logistic regression model selection, improving reliability and transparency.

A look at the effectiveness and limitations of debiased shrinkage estimators.

Explore the significance and applications of operator-valued orthogonal polynomials in mathematics.

Exploring the benefits of Wasserstein distances in machine learning model generalisation.

Examine the impacts of shifting data sources on official statistics and machine learning.

A new approach to train models effectively despite noisy labels.

This article discusses using mini-batches with Gaussian processes to solve nonlinear PDEs.

A study on estimating and testing monotonic density functions using Bayesian approaches.

A look at how Bayesian methods improve density estimation under shape restrictions.

Learn how Markov chains model systems with slow changes over time.

A review of the evolution and practical use of Markov bases in data sampling.