Add to ORKGIn this paper, a method for estimating monotone, convex and log-concave densities is proposed. The estimation procedure consists of an unconstrained kernel estimator which is modified in a second step with respect to the desired shape constraint by using monotone rearrangements. It is shown that the resulting estimate is a density itself and shares the asymptotic properties of the unconstrained estimate. A short simulation study shows the finite sample behavior
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
The estimation of a log-concave density on R is a canonical problem in the area of shape-constrained...
We tackle the problem of high-dimensional nonparametric density estimation by taking the class of lo...
In this paper, a method for estimating monotone, convex and log-concave densities is proposed. The ...
. The paper deals with a new aspect of density estimation by the kernel type method. Namely, shape p...
In the early years of kernel density estimation, Watson and Leadbetter (1963) attempted to optimize ...
Thesis (Ph.D.)--University of Washington, 2013We consider inference about functions estimated via sh...
We suggest a method for rendering a standard kernel density estimator unimodal: tilting the empirica...
Abstract. We consider a problem of nonparametric density estimation under shape restrictions. The fi...
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Nonparametric density estimators are used to estimate an unknown probability density while making mi...
In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a f...
<p>A method is proposed for shape-constrained density estimation under a variety of constraints, inc...
This dissertation is based on the development of methods for statistical problems with inherent shap...
Key words and phrases Nonparametric density estimation monotone density symmetric unimodal density...
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
The estimation of a log-concave density on R is a canonical problem in the area of shape-constrained...
We tackle the problem of high-dimensional nonparametric density estimation by taking the class of lo...
In this paper, a method for estimating monotone, convex and log-concave densities is proposed. The ...
. The paper deals with a new aspect of density estimation by the kernel type method. Namely, shape p...
In the early years of kernel density estimation, Watson and Leadbetter (1963) attempted to optimize ...
Thesis (Ph.D.)--University of Washington, 2013We consider inference about functions estimated via sh...
We suggest a method for rendering a standard kernel density estimator unimodal: tilting the empirica...
Abstract. We consider a problem of nonparametric density estimation under shape restrictions. The fi...
In Statistics, log-concave density estimation is a central problem within the field of nonparametric...
Nonparametric density estimators are used to estimate an unknown probability density while making mi...
In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a f...
<p>A method is proposed for shape-constrained density estimation under a variety of constraints, inc...
This dissertation is based on the development of methods for statistical problems with inherent shap...
Key words and phrases Nonparametric density estimation monotone density symmetric unimodal density...
It is well known now that kernel density estimators are not consistent when estimat-ing a density ne...
The estimation of a log-concave density on R is a canonical problem in the area of shape-constrained...
We tackle the problem of high-dimensional nonparametric density estimation by taking the class of lo...