Edgeworth Approximation of Multivariate Differential Entropy

DOI10.1162/0899766054323026zbMath1076.62013OpenAlexW2141652941WikidataQ51966299 ScholiaQ51966299MaRDI QIDQ5706652

Marc M. Van Hulle

Publication date: 21 November 2005

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/0899766054323026

Mathematics Subject Classification ID

Approximations to statistical distributions (nonasymptotic) (62E17) Statistical aspects of information-theoretic topics (62B10)

Related Items (15)

Expansions for log densities of multivariate estimates ⋮ Normality-based validation for crisp clustering ⋮ Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation ⋮ Minimum mutual information and non-Gaussianity through the maximum entropy method: theory and properties ⋮ Detecting direct associations in a network by information theoretic approaches ⋮ The skewness of mean-variance normal mixtures ⋮ Parametric Bayesian estimation of differential entropy and relative entropy ⋮ Particle-kernel estimation of the filter density in state-space models ⋮ Improved Approximation of the Sum of Random Vectors by the Skew Normal Distribution ⋮ Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search ⋮ Dimensionality reduction for density ratio estimation in high-dimensional spaces ⋮ Machine learning with squared-loss mutual information ⋮ Differential Log Likelihood for Evaluating and Learning Gaussian Mixtures ⋮ Information-Theoretic Representation Learning for Positive-Unlabeled Classification ⋮ Singular value decomposition of the third multivariate moment

Cites Work

• A Mathematical Theory of Communication
• Projection pursuit
• Independent component analysis, a new concept?
• What is Projection Pursuit?
• A nonparametric estimation of the entropy for absolutely continuous distributions (Corresp.)
• Estimation of Entropy and Mutual Information
• Limit theorems for sums of general functions of m-spacings
• Adaptive Blind Deconvolution of Linear Channels Using Renyi's Entropy with Parzen Window Estimation

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