Constructing irregular histograms by penalized likelihood

Y. Rozenholc, Thoralf Mildenberger, U. Gather
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引用次数: 5

Abstract

We propose a fully automatic procedure for the construction of irregular histograms. For a given number of bins, the maximum likelihood histogram is known to be the result of a dynamic programming algorithm. To choose the number of bins, we propose two different penalties motivated by recent work in model selection by Castellan [6] and Massart [26]. We give a complete description of the algorithm and a proper tuning of the penalties. Finally, we compare our procedure to other existing proposals for a wide range of different densities and sample sizes.
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用惩罚似然法构造不规则直方图
我们提出了一种完全自动化的不规则直方图构造方法。对于给定数量的箱子,已知最大似然直方图是动态规划算法的结果。为了选择箱子的数量,我们提出了两种不同的惩罚,这是由Castellan[6]和Massart[26]最近在模型选择方面的工作所激发的。我们给出了一个完整的算法描述和适当的调整惩罚。最后,我们将我们的程序与其他现有的建议进行了比较,以适应不同密度和样本量的范围。
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