In many applications,assumptions about the log-concavity of a probability distribution allow just enough special structure to yield a workable theory.This paper catalogs a series of theorems relating log-concavity and/or log-convexity of probability density functions,distribution functions,reliability functions,and their integrals. We list a large number of commonly-used probability distributions and report the log-concavity or log-convexity of their density functions and their integrals.We also discuss a variety of applications of log-concavity that have appeared in the literature
Log-concavity of a joint survival function is proposed as a model for bivariate increasing failure r...
We study nonparametric maximum likelihood estimation of a log-concave probability density and its di...
Thesis (Ph.D.)--University of Washington, 2013We consider inference about functions estimated via sh...
In many applications, assumptions about the log-concavity of a probability distribution allow just e...
Interesting properties and propositions, in many branches of science such as economics have been ob...
Nonparametric statistics for distribution functions F or densities f=F' under qualitative shape cons...
For a sequence of n independent events, it is shown that the sequences of the probability of occurre...
Abstract: We review and formulate results concerning log-concavity and strong-log-concavity in both ...
We study probability density functions that are log-concave. Despite the space of all such densities...
The assumption of log-concavity is a flexible and appealing non-parametric shape constraint in distr...
In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a f...
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric app...
We consider a novel sub-class of log-location-scale models for survival and reliability data formed ...
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the...
Log-concavity of a joint survival function is proposed as a model for bivariate increasing failure r...
We study nonparametric maximum likelihood estimation of a log-concave probability density and its di...
Thesis (Ph.D.)--University of Washington, 2013We consider inference about functions estimated via sh...
In many applications, assumptions about the log-concavity of a probability distribution allow just e...
Interesting properties and propositions, in many branches of science such as economics have been ob...
Nonparametric statistics for distribution functions F or densities f=F' under qualitative shape cons...
For a sequence of n independent events, it is shown that the sequences of the probability of occurre...
Abstract: We review and formulate results concerning log-concavity and strong-log-concavity in both ...
We study probability density functions that are log-concave. Despite the space of all such densities...
The assumption of log-concavity is a flexible and appealing non-parametric shape constraint in distr...
In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a f...
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric app...
We consider a novel sub-class of log-location-scale models for survival and reliability data formed ...
Maximum likelihood estimation of a log-concave density has attracted considerable attention over the...
Log-concavity of a joint survival function is proposed as a model for bivariate increasing failure r...
We study nonparametric maximum likelihood estimation of a log-concave probability density and its di...
Thesis (Ph.D.)--University of Washington, 2013We consider inference about functions estimated via sh...