Latest Articles for Statistical Modeling

MultiCOAP improves analysis of count data from multiple studies, addressing complexity and overdispersion.

A novel method improves Bayesian network learning from data.

New method enhances Gaussian process models for faster predictions with real-time data.

A look at CARMA processes and their simulation using tempered stable distributions.

Introducing SARIS: a method for estimating normalizing constants with improved efficiency.

Examining how added variables influence statistical significance in research findings.

Researchers enhance data generation through the study of Conditional Hierarchical Variational Autoencoders.

This study presents methods for Bayesian optimization across different search spaces.

A study reveals new methods to analyze cancer death rates in Colombia.

A new tool for studying events over space and time.

A new approach to analyzing political violence improves risk assessment and response strategies.

A look at kernel Kullback-Leibler divergence and its practical applications.

A new approach to estimate treatment effects considering social influences.

A new method improves clustering by focusing on covariance matrices in various fields.

A new approach for modeling spatial data with direction-specific relationships.

Learn how BCART models improve claim prediction in insurance.

A fresh method identifies groups in complex behavioral data for better insights.

This article discusses the shift to continuous models in data analysis.

Learn how to analyze panel data using partially observed Markov processes.

Introducing Conditional CRPS for improved predictions of interrelated outcomes.

This research evaluates a new model for estimating treatment effects in individuals.

A new algorithm improves efficiency in Bayesian experimental design.

A new approach to gradual changes in financial networks enhances investment strategies.

A look at how PCR enhances prediction accuracy in statistical models.

A new method for selecting statistical models aims for simplicity and improved accuracy.

BMRMM aids researchers in analyzing complex sequences influenced by time and various factors.

A new method improves predictions by addressing channel dependencies over time.

A comparison of filtering methods reveals new insights on model effectiveness.

Exploring how Deep Gaussian Processes improve predictions by addressing complex data relationships.

A novel approach improves estimates of how COVID-19 spreads.

A study on predicting potential earthquake damage in Turkey.

Exploring the impact of detrending methods on extreme temperature values.

New methods improve the accuracy of estimating conditional densities in complex data.

Using weighted likelihood to enhance understanding of killer whale foraging.

This article discusses methods for learning unnormalized distributions using noise-contrastive estimation.

This article presents a new method for identifying changes in event data.

Analyzing the impact of vaccination and socioeconomic factors on COVID-19 trends.

New modeling methods enhance understanding of neuronal activity and external factors.

A Python package for easy moment calculations in financial modeling.

Exploring the importance of prediction sets in drug development.