Dual representation of \(\phi\)-divergences and applications.

DOI10.1016/S1631-073X(03)00215-2zbMath1043.62002MaRDI QIDQ1408180

Amor Keziou

Publication date: 15 September 2003

Published in: Comptes Rendus. Mathématique. Académie des Sciences, Paris (Search for Journal in Brave)

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10) Statistical aspects of information-theoretic topics (62B10)

Related Items (23)

A Weighted Bootstrap Procedure for Divergence Minimization Problems ⋮ Robustness of dual divergence estimators for models satisfying linear constraints ⋮ Class-prior estimation for learning from positive and unlabeled data ⋮ Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation ⋮ Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation ⋮ Minimum divergence estimators, maximum likelihood and exponential families ⋮ A Deep Generative Approach to Conditional Sampling ⋮ Dual divergences estimation for censored survival data ⋮ Empirical likelihood with censored data ⋮ Dual divergence estimators of the tail index ⋮ Divergences and duality for estimation and test under moment condition models ⋮ Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization ⋮ On Bayesian estimation via divergences ⋮ Semi-supervised learning of class balance under class-prior change by distribution matching ⋮ Inference in ϕ-families of distributions ⋮ On empirical likelihood for semiparametric two-sample density ratio models ⋮ Parametric estimation and tests through divergences and the duality technique ⋮ Test of homogeneity in semiparametric two-sample density ratio models ⋮ Change-point detection in time-series data by relative density-ratio estimation ⋮ Estimation and tests of independence in copula models via divergences ⋮ General bootstrap for dual \(\phi\)-divergence estimates ⋮ Information-Theoretic Representation Learning for Positive-Unlabeled Classification ⋮ New estimates and tests of independence in some copula models

Cites Work

• Efficiency versus robustness: The case for minimum Hellinger distance and related methods
• Asymptotic divergence of estimates of discrete distributions
• Asymptotic Statistics
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