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Choosing a good histogram

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Histograms are probably among the most simple and popular estimators. They are widely used in science, especially by non-statisticians, in view of estimating densities (or intensities of point processes). To build a good histogram, one needs to partition the data in a suitable way which turns out to be a tricky problem. Given a (possibly large) family of candidate partitions, how can we select a suitable one on which our histogram will be as close as possible to the unknown density? Besides, are there families of partitions one should consider preferably? These are some of the questions we shall try to answer in this talk by adopting a non-asymptotic point of view.

This talk is part of the Statistics series.

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