Abstract
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 the rule works in practice.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 49-59 |
| Number of pages | 11 |
| Journal | Journal of Statistical Planning and Inference |
| Volume | 61 |
| Issue number | 1 |
| DOIs | |
| State | Published - May 30 1997 |
Bibliographical note
Funding Information:* Corresponding author. Research supported in part by NSF Grant SES 9201718. 1 Research supported in part by University of Kansas General Research Fund.
Keywords
- Admissibility
- Bayesian bootstrap
- Histogram
- Non-informative Bayes and entropy
- Stepwise Bayes
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