L
Lutz Duembgen
Researcher at University of Bern
Publications - 32
Citations - 657
Lutz Duembgen is an academic researcher from University of Bern. The author has contributed to research in topics: Estimator & Distribution function. The author has an hindex of 10, co-authored 28 publications receiving 585 citations.
Papers
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Maximum likelihood estimation of a log-concave density and its distribution function: Basic properties and uniform consistency
Lutz Duembgen,Kaspar Rufibach +1 more
TL;DR: In this article, the authors study nonparametric maximum likelihood estimation of a log-concave probability density and its distribution and hazard function, and show that the rate of convergence with respect to supremum norm on a compact interval for the density and hazard rate estimator is at least
Journal ArticleDOI
A simple asthma prediction tool for preschool children with wheeze or cough
Anina M. Pescatore,Cristian Dogaru,Lutz Duembgen,Michael Silverman,Erol A. Gaillard,Ben D. Spycher,Claudia E. Kuehni +6 more
TL;DR: This tool represents a simple, low-cost, and noninvasive method to predict the risk of later asthma in symptomatic preschool children, which is ready to be tested in other populations.
Journal ArticleDOI
Multiscale inference about a density
Lutz Duembgen,Guenther Walther +1 more
TL;DR: In this paper, a multiscale test statistic based on local order statistics and spacings is introduced that provides simultaneous confidence statements for the existence and location of local increases and decreases of a density or a failure rate.
Journal ArticleDOI
Approximation by log-concave distributions, with applications to regression
TL;DR: It is shown that an approximation of arbitrary distributions P on d-dimensional space by distributions with log-concave density exists if and only if P has finite first moments and is not supported by some hyperplane, and that this approximation depends continuously on P with respect to Mallows distance D 1.
Posted Content
Active Set and EM Algorithms for Log-Concave Densities Based on Complete and Censored Data
TL;DR: An active set algorithm for the maximum likelihood estimation of a log-concave density based on complete data and an EM algorithm to treat arbitrarily censored or binned data are developed.