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

✍ Scribed by Kun He; Glen Meeden

Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 575 KB
Volume: 61
Category: Article
ISSN: 0378-3758
DOI: 10.1016/s0378-3758(96)00142-5

No coin nor oath required. For personal study only.

✦ Synopsis

In this note we consider the problem of, given a sample, selecting the number of bins in a histogram. A loss function is introduced which reflects the idea that smooth distributions should have fewer bins than rough distributions. A stepwise Bayes rule, based on the Bayesian bootstrap, is found and is shown to be admissible. Some simulation results are presented to show how lhe rule works in practice.

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