\texttt{fastMI}: a fast and consistent copula-based nonparametric estimator of mutual information
From MaRDI portal
Publication:6200945
DOI10.1016/j.jmva.2023.105270arXiv2212.10268MaRDI QIDQ6200945
Peter X.-K. Song, Soumik Purkayastha
Publication date: 25 March 2024
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.10268
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Measuring and testing dependence by correlation of distances
- A Mathematical Theory of Communication
- Detecting Novel Associations in Large Data Sets
- Probit transformation for nonparametric kernel estimation of the copula density
- Bandwidth selection for kernel density estimation: a review of fully automatic selectors
- A fast and objective multidimensional kernel density estimation method: fastKDE
- Analyzing dependent data with vine copulas. A practical guide with R
- Equitability, mutual information, and the maximal information coefficient
- Dependence Modeling with Copulas
- Information Theoretical Analysis of Multivariate Correlation
- Self-Consistent Method for Density Estimation
- Estimation of Entropy and Mutual Information
- Selected Topics in Characteristic Functions
- Jackknife approach to the estimation of mutual information
- Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances
- Relationship Between Kendall's tau Correlation and Mutual Information
- A consistent multivariate test of association based on ranks of distances
- On the Estimation of the Probability Density, I
- On the Amount of Information
- The Hellinger Correlation
This page was built for publication: \texttt{fastMI}: a fast and consistent copula-based nonparametric estimator of mutual information
