Statistics

A study on the limitations of Statcheck in verifying statistical results.

Researchers evaluate how emission lines reveal conditions in giant molecular clouds.

Exploring the significance of Brownian motion in various fields.

A study develops a model to improve colorectal cancer screening using risk factors.

Scientists develop improved techniques for analyzing dark matter signals.

Examining the impact of road networks on urban development in Australia.

This study examines the effectiveness of Lasso in high-dimensional diffusion models.

Analyzing gun violence patterns to improve community safety strategies.

A study on the limitations of Statcheck in verifying statistical results.

Introducing a flexible model for estimating volatility across various fields.

O-ISAC merges communication and sensing for smarter technology.

New methods reduce time for analyzing complex networks effectively.

Researchers assess risks of identifying individuals from shared data to improve privacy.

Air-HOLP improves feature screening in complex datasets, balancing accuracy and efficiency.

This article discusses new methods for efficient data analysis using Bayesian techniques.

Semi-Markov models improve understanding of cancer latency and overdiagnosis risks.

A method to classify complex time series data using spectral analysis.

This article examines how intrinsic dimension influences neural network performance across image types.

A new method reveals insights into prediction errors and model complexity.

Innovative approach to enhance diffusion models while avoiding complex adjustments.

A new Bayesian approach enhances modeling of complex systems and their dynamics.

SwipeRx uses AI for personalized pharmacy recommendations and improved patient care.

A new method enhances active learning in graph-based tasks with limited labeling.

New error bounds improve GPR's reliability in safety-sensitive fields.

Combining study results enables clearer conclusions through p-value analysis.

A method to classify complex time series data using spectral analysis.

Innovative approaches improve analysis and efficiency in clinical trials.

This paper discusses methods to optimize experimental design for better efficiency and outcomes.

A fresh approach to causal inference using flexible data collection methods.

A method to simplify regression model selection amidst uncertainty.

Learn how the Deep Limit Model-free method improves predictions and uncertainty management.

This article examines how Bayesian and frequentist methods handle outliers in data analysis.

Examining how scatterers affect wireless signal quality in various environments.

Learn how motion affects wireless communication signals and their reliability.

Discover strategies to improve your odds in Hopscotch and Warships.

New forecasting methods enhance demand prediction accuracy and inventory management.

A guide to efficiently format academic submissions using elsarticle.cls.

A look at the Ornstein-Uhlenbeck process and its real-world applications.

New adaptive detection algorithms improve target identification in complex environments.

A new method improves estimation of population characteristics using mixed sampling data.

Exploring the significance of Brownian motion in various fields.

A new method reveals insights into prediction errors and model complexity.

Explore the role of eigenvalues in improving statistical analyses and model evaluations.

Examining the impact of learning rates on predictive performance.

This study examines the effectiveness of Lasso in high-dimensional diffusion models.

New method enhances accuracy in analyzing time series data.

A look at the complexities of normal mixture models in statistics.

A fresh approach to causal inference using flexible data collection methods.