Discrete mixture representations of parametric distribution families: geometry and statistics
From MaRDI portal
Publication:2219222
DOI10.1214/20-EJS1795zbMath1453.60023MaRDI QIDQ2219222
Ludwig Baringhaus, Rudolf Grübel
Publication date: 19 January 2021
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ejs/1609902186
EM algorithmasymptotic efficiencyFisher informationmixture distributionbarycenter convexitychi-squared distribution familiesestimation of mixing probabilities
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Consistency of maximum likelihood estimators for certain nonparametric families, in particular: Mixtures
- Estimation of the non-centrality parameter of a chi squared distribution
- Extremal families and systems of sufficient statistics
- Binomial mixtures and finite exchangeability
- Consistent estimation of a mixing distribution
- Defining the curvature of a statistical problem (with applications to second order efficiency)
- Sufficient statistics and extreme points
- Comparison of experiments and information measures
- Nonparametric estimation of convex models via mixtures
- Binomial mixtures: Geometric estimation of the mixing distribution
- A unified approach to estimation of noncentrality parameters, the multiple correlation coefficient, and mixture models
- Differential geometry of curved exponential families. Curvatures and information loss
- Prior distributions on spaces of probability measures
- Mixture representations of noncentral distributions
- Non-Equivalent Comparisons of Experiments and Their Use for Experiments Involving Location Parameters
- Fundamentals of Nonparametric Bayesian Inference
- Lectures on Choquet's theorem
This page was built for publication: Discrete mixture representations of parametric distribution families: geometry and statistics
