Mathematics
4 years ago

New method detects independence in stochastic processes, revolutionizing data analysis.

Bernoulli
Distance covariance for discretized stochastic processes
Herold Dehling, Muneya Matsui, Thomas Mikosch, Gennady Samorodnitsky, Laleh Tafakori

Paper Summary

Related papers

Theme background image
Mathematics
2 years ago

Revolutionizing galaxy correlation studies: Fast covariance method promises precision boost!

Multicollinearity
Galaxy Evolution
Regularization Methods
2 views
Theme background image
Chemistry
5 years ago

New method detects hidden relationships between processes, revolutionizing data analysis!

Chemometrics
Process Fault Detection
Multicollinearity
1 view
Theme background image
Mathematics
5 years ago

New algorithm makes distance correlation faster and more accessible for all!

Regularization Methods
Multicollinearity
Model-Based Clustering

The article introduces a way to measure how independent two sets of random processes are. By looking at how these processes change over time and comparing them at different points, researchers can tell if they are related or not. They found that if the processes are independent, the measure they created will be zero. They also developed a method to test if the processes are truly independent, which showed reliable results.