Statistics

A look into identifying regional-colocation patterns and minimizing false discoveries.

The FLAP method enhances forecasting by using existing data in new ways.

A framework for better estimating treatment effects in paired cluster-randomized experiments.

A new method significantly speeds up sampling from non-Gaussian Lévy processes.

Learn strategies to enhance A/B testing practices for improved business decisions.

A guide on differentiating natural effects from randomized interventional analogues.

Effective methods for quantifying CO2 removal using enhanced rock weathering techniques.

A new method enhances the accuracy of precipitation forecasts for better decision-making.

A new method for tuning hyperparameters using Bayesian ideas.

The FLAP method enhances forecasting by using existing data in new ways.

A novel approach to study time series data and its factors in healthcare.

Efficient modeling techniques enhance engineering design processes and reduce costs.

A new method significantly speeds up sampling from non-Gaussian Lévy processes.

A look at the benefits of expectation propagation for statistical analysis.

This article discusses methods for estimating quantiles in directional data.

A new method for identifying change points in data patterns using continuous optimization.

Introducing a method to simplify variable relationships in graphical models through clustering.

A new method for tuning hyperparameters using Bayesian ideas.

Efficient modeling techniques enhance engineering design processes and reduce costs.

This research enhances Gaussian Processes using adaptive Fourier features for better predictions.

Learn about causal structures and the impact of unmeasurable variables.

Examining how deep belief networks can learn from data and create complex representations.

Techniques for managing hyperparameters and model weights for improved performance.

New loss functions aim to enhance density ratio estimation in machine learning.

The FLAP method enhances forecasting by using existing data in new ways.

A framework for better estimating treatment effects in paired cluster-randomized experiments.

A novel approach to study time series data and its factors in healthcare.

A review of methods to improve causal inference using RCTs and RWD.

New statistical tests improve analysis in response-adaptive clinical trials.

NCP simplifies learning conditional probabilities for better decision-making across fields.

Learn strategies to enhance A/B testing practices for improved business decisions.

This method helps pinpoint groups that respond best to treatments.

A fresh approach to understanding complex human connections through fuzzy social network analysis.

A study on how Park-and-Ride can reduce congestion and emissions.

Information significantly impacts decision-making processes across various fields.

Benford's Law reveals odd patterns in first digits across data sets.

A method for detailed signal analysis over time and frequency.

New methods enhance understanding of the universe through weak gravitational lensing data.

A study on how information travels through social networks with community structures.

Examining how scatterers affect wireless signal quality in various environments.

NCP simplifies learning conditional probabilities for better decision-making across fields.

A look at model evaluation methods and their effectiveness.

A look at structured covariance matrices and their role in analyzing variable relationships.

Exploring the impact of sparse random projections on high-dimensional data analysis.

A detailed look at clustering methods and their definitions.

Discover simplified methods for calculating cross-partial derivatives in complex functions.

Research on neural networks reveals behaviors of heavy-tailed distributions.

Introducing a novel filtering technique for non-Gaussian systems.