summary.dlc

An object of class <code>"dlc"</code>, usually a result of a call to <code>logConDens</code>.

object

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<code>summary</code> method for class <code>"dlc"</code>.

htest

nonparametric

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

Kaspar Rufibach

logcondens

Estimate a Log-Concave Probability Density from Iid Observations

summary.dlc function

Note that the numbering of knots in the output relies on the vector of unique observations.

summary.dlc: Summarizing log-concave density estimation

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