Výsledky vyhledávání - "Lutz Duembgen"

A simple asthma prediction tool for preschool children with wheeze or cough

Autor: Michael Silverman, Anina M. Pescatore, Lutz Duembgen, Ben D. Spycher, Erol A. Gaillard, Cristian Dogaru, Claudia E. Kuehni

Publikováno v: Journal of Allergy and Clinical Immunology
The Journal of allergy and clinical immunology

Background Many preschool children have wheeze or cough, but only some have asthma later. Existing prediction tools are difficult to apply in clinical practice or exhibit methodological weaknesses. Objective We sought to develop a simple and robust t

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Least squares and shrinkage estimation under bimonotonicity constraints

Autor: Rudolf Beran, Lutz Duembgen

Publikováno v: Statistics and Computing
Beran, Rudolf; Dümbgen, Lutz (2010). Least Squares and Shrinkage Estimation under Bimonotonicity Constraints. Statistics and computing, 20(2), pp. 177-189. New York, N.Y.: Springer US; http://www.springer-ny.com 10.1007/s11222-009-9124-0

In this paper we describe active set type algorithms for minimization of a smooth function under general order constraints, an important case being functions on the set of bimonotone r×s matrices. These algorithms can be used, for instance, to estim

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbca96d38e1e3d1f087b31a5d41cef07
https://doi.org/10.1007/s11222-009-9124-0

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Maximum-Likelihood Estimation of a Log-Concave Density based on Censored Data

Autor: Chun Yip Yau, Lutz Duembgen, Kaspar Rufibach, Ngai Hang Chan

Publikováno v: Electronic Journal of Statistics
Electron. J. Statist. 8, no. 1 (2014), 1405-1437
Dümbgen, Lutz; Rufibach, Kaspar; Schuhmacher, Dominic (2014). Maximum-Likelihood Estimation of a Log-Concave Density based on Censored Data. Electronic journal of statistics, 8(1), pp. 1405-1437. Institute of Mathematical Statistics 10.1214/14-EJS930

We consider nonparametric maximum-likelihood estimation of a log-concave density in case of interval-censored, right-censored and binned data. We allow for the possibility of a subprobability density with an additional mass at $+\infty$, which is est

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http://arxiv.org/abs/1311.6403

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An analysis of variance type method to describe and compare steady states in clinical data

Autor: Andreas Hengstenberg, Dammon Ziaian, Lutz Duembgen, Astrid Ellen Berggreen, M. Grossherr, Hartmut Gehring, Stefan Zimmermann

Publikováno v: 2013 E-Health and Bioengineering Conference (EHB).

Identifying and comparing different steady states is an important task for clinical decision making. Data from unequal sources, comprising diverse patient status information, have to be interpreted. In order to compare results an expressive represent

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https://doi.org/10.1109/ehb.2013.6707416

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Is computer-assisted telephone triage safe? A prospective surveillance study in walk-in patients with non-life-threatening medical conditions

Autor: Andreas Meer, Heinz Zimmermann, Lutz Duembgen, Thomas Gwerder, Niki Zumbrunnen

Publikováno v: Emergency medicine journal : EMJ. 29(2)

Background Patients often establish initial contact with healthcare institutions by telephone. During this process they are frequently medically triaged. Purpose To investigate the safety of computer-assisted telephone triage for walk-in patients wit

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https://pubmed.ncbi.nlm.nih.gov/20961939

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Extensions of smoothing via taut strings

Autor: Arne Kovac, Lutz Duembgen

Publikováno v: Electron. J. Statist. 3 (2009), 41-75
Electronic Journal of Statistics

Suppose that we observe independent random pairs $(X_1,Y_1)$, $(X_2,Y_2)$, >..., $(X_n,Y_n)$. Our goal is to estimate regression functions such as the conditional mean or $\beta$--quantile of $Y$ given $X$, where $00$ is some tuning parameter. This f

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https://projecteuclid.org/euclid.ejs/1233176790

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Multivariate Log-Concave Distributions as a Nearly Parametric Model

Autor: Lutz Duembgen, Dominic Schuhmacher, Andre Huesler

Publikováno v: Schuhmacher, Dominic; Hüsler, André; Dümbgen, Lutz (2009). Multivariate Log-Concave Distributions as a Nearly Parametric Model (Technical Report 74). Bern: Institut für mathematische Statistik und Versicherungslehre der Universität Bern (IMSV)
Schuhmacher, Dominic; Hüsler, André; Dümbgen, Lutz (2011). Multivariate Log-Concave Distributions as a Nearly Parametric Model. Statistics & risk modeling, 28(3), pp. 277-295. Berlin: Oldenbourg Wissenschaftsverlag GmbH 10.1524/stnd.2011.1073
Statistics and Risk Modeling

In this paper we show that the family P_d of probability distributions on R^d with log-concave densities satisfies a strong continuity condition. In particular, it turns out that weak convergence within this family entails (i) convergence in total va

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f357c9252c8fec4903c4cc145981213
https://boris.unibe.ch/36864/1/WeakContLogConcTR.pdf

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Increased densities of monocarboxylate transport protein MCT1 after chronic administration of nicotine in rat brain

Autor: Roman Duelli, Brigitte Mack, Martin H. Maurer, Wolfgang Kuschinsky, Olivier Gires, Martin Canis, Lutz Duembgen

Publikováno v: Neuroscience research. 64(4)

Chronic administration of nicotine is followed by a general stimulation of brain metabolism that results in a distinct increase of glucose transport protein densities for Glut1 and Glu3, and local cerebral glucose utilization (LCGU). This increase of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::708a2d4828846f65e30976302d3b21e2
https://pubmed.ncbi.nlm.nih.gov/19433117

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Increased densities of monocarboxylate transporter MCT1 after chronic hyperglycemia in rat brain

Autor: Lutz Duembgen, Martin H. Maurer, Roman Duelli, Wolfgang Kuschinsky, Martin Canis

Publikováno v: Brain research. 1257

The brain is capable of taking up monocarboxylates as energy substrates. Under physiological conditions, plasma levels of monocarboxylates are very low and glucose is the primary energy substrate in brain metabolism. However, given conditions such as

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54f2c279ab638f04e6a72f9c2b3f1406
https://pubmed.ncbi.nlm.nih.gov/19118535

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