Estimating the joint distribution of independent categorical variables via model selection

DOI10.3150/08-BEJ155zbMath1200.62024arXiv0906.2275OpenAlexW3099142217MaRDI QIDQ605871

Cécile Durot, Anne-Sophie Tocquet, Emilie Lebarbier

Publication date: 15 November 2010

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0906.2275

zbMATH Keywords

model selectionwaveletadaptive estimatorleast-squares estimatorcategorical variablemultinomial variablepenalized minimum contrast estimator

Mathematics Subject Classification ID

Density estimation (62G07) Estimation in multivariate analysis (62H12) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Nonparametric estimation (62G05)

Uses Software

• MuGeN

Cites Work

• Detecting multiple change-points in the mean of Gaussian process by model selection
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• Minimum contrast estimators on sieves: Exponential bounds and rates of convergence
• Concentration inequalities using the entropy method
• Minimax estimation via wavelet shrinkage
• Bootstrapping a nonparametric polytomous regression model
• An adaptive compression algorithm in Besov spaces
• Minimal penalties for Gaussian model selection
• Maximum likelihood estimation of multiple change points
• Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation
• Estimating a discrete distributionviahistogram selection
• Gaussian model selection

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