Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Comp Stat

Computation of the nonparametric maximum likelihood estimate of a univariate log‐concave density

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Algorithms for computing the nonparametric maximum likelihood estimate of a univariate log‐concave density are briefly described, and some relevant issues discussed. The fast few algorithms are further numerically compared in a small‐scale simulation study.

This article is categorized under:

  • Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)
  • Statistical and Graphical Methods of Data Analysis > Density Estimation
  • Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
  • Algorithms and Computational Methods > Algorithms
Density, log‐density and gradient functions of the nonparametric maximum likelihood estimators (NPMLEs) obtained from generic samples (n = 1,000) drawn from Exponential(1) and Normal(0, 1), respectively. Knots are indicated by small circles
[ Normal View | Magnified View ]

Browse by Topic

Algorithms and Computational Methods > Algorithms
Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)
Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Statistical and Graphical Methods of Data Analysis > Density Estimation

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts