BibTeX
@MISC{_statisticalcomputing,
author = {},
title = {STATISTICAL COMPUTING AND GRAPHICS Data-Based Choice of Histogram Bin Width},
year = {}
}
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Abstract
The most important parameter of a histogram is the bin width because it controls the tradeoff between presenting a picture with too much detail ("undersmoothing") or too little detail ("oversmoothing") with respect to the true distri-bution. Despite this importance there has been surprisingly little research into estimation of the "optimal " bin width. Default bin widths in most common statistical packages are, at least for large samples, quite far from the optimal bin width. Rules proposed by, for example, Scott lead to better large sample performance of the histogram, but are not consistent themselves. In this paper we extend the bin width rules of Scott to those that achieve root-n rates of convergence to the L2-optimal bin width, thereby provid-ing firm scientific justification for their use. Moreover, the proposed rules are simple, easy and fast to compute, and perform well in simulations. KEY WORDS: Binning; Data analysis; Density estima-tion; Kernel functional estimator; Smoothing parameter se-lection. 1.
Keyphrases
histogram bin width statistical computing graphic data-based choice optimal bin width large sample performance little detail bin width large sample kernel functional estimator l2-optimal bin width common statistical package bin width rule default bin width true distri-bution important parameter data analysis much detail provid-ing firm scientific justification density estima-tion root-n rate parameter se-lection little research key word
