Journal
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume 77, Issue 7, Pages 561-574Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10629360600569097
Keywords
non-parametric density estimation; shape restriction; interior point method; iterative convex minorant algorithm; Newton method on subspace
Ask authors/readers for more resources
We consider the problem of estimating a density function that is assumed to be log- concave. This semi-parametric model includes many well- known parametric classes; such as Normal, Gamma, Laplace, Logistic, Beta or Extreme value distributions, for specific parameter ranges. It is known that the maximum likelihood estimator for the log- density is always a piecewise linear function with at most as many knots as observations, but typically much less. We show that this property can be exploited to design a linearly constrained optimization problem whose iteratively calculated solution yields the estimator. We compare several standard and one recently proposed algorithm regarding their performance on this problem.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available