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Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach
| Content Provider | Semantic Scholar |
|---|---|
| Author | Meeden, Glen |
| Copyright Year | 1997 |
| Abstract | 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. |
| Starting Page | 49 |
| Ending Page | 59 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/S0378-3758(96)00142-5 |
| Volume Number | 61 |
| Alternate Webpage(s) | http://users.stat.umn.edu/~gmeeden/papers/hist.pdf |
| Alternate Webpage(s) | http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=397B0683BABD52886E359E144F87A595?doi=10.1.1.43.9947&rep=rep1&type=pdf |
| Alternate Webpage(s) | http://www.stat.umn.edu/~glen/papers/hist.pdf |
| Alternate Webpage(s) | http://www.stat.umn.edu/~glen/papers/hist.ps |
| Alternate Webpage(s) | https://doi.org/10.1016/S0378-3758%2896%2900142-5 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
