logcondens: Computations Related to Univariate Log-Concave Density Estimation
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 l...
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doaj-4108383dbe2b4f11afb45487fc398e222020-11-24T22:59:45ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602011-03-013906logcondens: Computations Related to Univariate Log-Concave Density EstimationLutz DümbgenKaspar RufibachMaximum 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.http://www.jstatsoft.org/v39/i06/paperlog-concavedensity estimationKolmogorov-Smirnov testR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lutz Dümbgen Kaspar Rufibach |
spellingShingle |
Lutz Dümbgen Kaspar Rufibach logcondens: Computations Related to Univariate Log-Concave Density Estimation Journal of Statistical Software log-concave density estimation Kolmogorov-Smirnov test R |
author_facet |
Lutz Dümbgen Kaspar Rufibach |
author_sort |
Lutz Dümbgen |
title |
logcondens: Computations Related to Univariate Log-Concave Density Estimation |
title_short |
logcondens: Computations Related to Univariate Log-Concave Density Estimation |
title_full |
logcondens: Computations Related to Univariate Log-Concave Density Estimation |
title_fullStr |
logcondens: Computations Related to Univariate Log-Concave Density Estimation |
title_full_unstemmed |
logcondens: Computations Related to Univariate Log-Concave Density Estimation |
title_sort |
logcondens: computations related to univariate log-concave density estimation |
publisher |
Foundation for Open Access Statistics |
series |
Journal of Statistical Software |
issn |
1548-7660 |
publishDate |
2011-03-01 |
description |
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. |
topic |
log-concave density estimation Kolmogorov-Smirnov test R |
url |
http://www.jstatsoft.org/v39/i06/paper |
work_keys_str_mv |
AT lutzdumbgen logcondenscomputationsrelatedtounivariatelogconcavedensityestimation AT kasparrufibach logcondenscomputationsrelatedtounivariatelogconcavedensityestimation |
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1725643941173264384 |