2016/10/30 by Gautier Marti, Marti, Gautier, Sébastien Andler +5 · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Psychology · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (stat.ML) #Mental Health Research Topics #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1610.09659
openalex publication_date 2016/10/30 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
We propose a methodology to explore and measure the pairwise correlations\nthat exist between variables in a dataset. The methodology leverages copulas\nfor encoding dependence between two variables, state-of-the-art optimal\ntransport for providing a relevant geometry to the copulas, and clustering for\nsummarizing the main dependence patterns found between the variables. Some of\nthe clusters centers can be used to parameterize a novel dependence coefficient\nwhich can target or forget specific dependence patterns. Finally, we illustrate\nand benchmark the methodology on several datasets. Code and numerical\nexperiments are available online for reproducible research.\n