Selecting the number of bins in a histogram: A decision theoretic approach

DOI10.1016/S0378-3758(96)00142-5zbMath0879.62002OpenAlexW2112154085MaRDI QIDQ1360972

Glen Meeden, Kun He

Publication date: 23 July 1997

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(96)00142-5

zbMATH Keywords

entropyhistogramBayesian bootstrapstepwise Bayesnon-informative Bayes

Mathematics Subject Classification ID

Density estimation (62G07) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10) Admissibility in statistical decision theory (62C15)

Related Items (7)

Bayesian networks and information theory for audio-visual perception modeling ⋮ A note on the e-a histogram ⋮ Histogram-kernel error and its application for bin width selection in histograms ⋮ How many bins should be put in a regular histogram ⋮ The \(q\)-exponentials do not maximize the Rényi entropy ⋮ A comparison of automatic histogram constructions ⋮ Histogram-based embedding for learning on statistical manifolds

Cites Work

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• Some admissible nonparametric and related finite population sampling estimators
• A stepwise Bayesian procedure
• A complete class theorem for statistical problems with finite sample spaces
• On optimal and data-based histograms
• On the Admissible Estimators for Certain Fixed Sample Binomial Problems

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