logcondens: Computations Related to Univariate Log-Concave Density Estimation
logcondens: Computations Related to Univariate Log-Concave Density Estimation
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logcondens：与单变量对数凹密度估计相关的计算
DOI：
10.18637/jss.v039.i06
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发表时间：
2011
影响因子：
5.8
通讯作者：
K. Rufibach
中科院分区：
文献类型：
--
作者：
L. Dümbgen;K. Rufibach
对数凹密度的极大似然估计在过去的几年里引起了相当大的关注。已经提出了几种算法来估计这样的密度。其中的两个算法，一个迭代凸minorant和一个活动集算法，在R包logcondens中实现。虽然这些算法在其他地方进行了讨论，我们在本文中描述了使用的logcondens包，并讨论函数和数据集相关的日志凹密度估计包中包含的。特别地，我们提供函数来（1）计算最大似然估计（MLE）以及从MLE导出的平滑对数凹密度估计，（2）评估任意点处的估计密度、分布和分位数函数，（3）计算MLE的特征函数，（4）从估计分布中采样，以及最后（5）使用修改的Kolmogorov-Smirnov检验统计量来执行双样本置换检验。此外，logcondens提供了两个数据集，用于说明对数凹密度估计。
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the last few years. Several algorithms have been proposed to estimate such a density. Two of those algorithms, an iterative convex minorant and an active set algorithm, are implemented in the R package logcondens. While these algorithms are discussed elsewhere, we describe in this paper the use of the logcondens package and discuss functions and datasets related to log-concave density estimation contained in the package. In particular, we provide functions to (1) compute the maximum likelihood estimate (MLE) as well as a smoothed log-concave density estimator derived from the MLE, (2) evaluate the estimated density, distribution and quantile functions at arbitrary points, (3) compute the characterizing functions of the MLE, (4) sample from the estimated distribution, and finally (5) perform a two-sample permutation test using a modified Kolmogorov-Smirnov test statistic. In addition, logcondens makes two datasets available that have been used to illustrate log-concave density estimation.
影响因子：
4.5
作者：
Seregin,Arseni;Wellner,JonA
通讯作者：
Wellner,JonA
影响因子：
4.5
作者：
Balabdaoui F;Rufibach K;Wellner JA
通讯作者：
Wellner JA