Editing Histogram (section)

==== Minimizing cross-validation estimated squared error ====

This approach of minimizing integrated mean squared error from Scott's rule can be generalized beyond normal distributions, by using leave-one out cross validation:<ref>{{Cite book|title=All of Statistics|last=Wasserman|first=Larry|publisher=Springer|year=2004|isbn=978-1-4419-2322-6|location=New York|pages=310}}</ref><ref>{{cite conference|url=http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf |title=An asymptotically optimal histogram selection rule |first=Charles J. |last=Stone |date=1984 |book-title=Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer }}</ref>

:<math>\underset{h}{\operatorname{arg\,min}} \hat{J}(h) = \underset{h}{\operatorname{arg\,min}} \left( \frac{2}{(n-1)h} - \frac{n+1}{n^2(n-1)h} \sum_k N_k^2 \right)</math>

Here, <math>N_k</math> is the number of datapoints in the ''k''th bin, and choosing the value of ''h'' that minimizes ''J'' will minimize integrated mean squared error.