Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach
By GLEN MEEDEN
University of Minnesota
and KUN HE
University of Minnesota
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.