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pro vyhledávání: '"Kalisch Markus"'
Akademický článek
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Autor:
Maathuis Marloes H, Grill Eva, Fellinghauer Bernd AG, Kalisch Markus, Mansmann Ulrich, Bühlmann Peter, Stucki Gerold
Publikováno v:
BMC Medical Research Methodology, Vol 10, Iss 1, p 14 (2010)
Abstract Background Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disabil
Externí odkaz:
https://doaj.org/article/a2c64a9f50ce44d2b605d65436d3e5e4
Publikováno v:
Comput Stat 35:1 (2020) 1-40
We provide a view on high-dimensional statistical inference for genome-wide association studies (GWAS). It is in part a review but covers also new developments for meta analysis with multiple studies and novel software in terms of an R-package hierin
Externí odkaz:
http://arxiv.org/abs/1805.02988
We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restrictions on a Markov equivalence class of directed acyclic graphs, or equivalently on its
Externí odkaz:
http://arxiv.org/abs/1707.02171
We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (
Externí odkaz:
http://arxiv.org/abs/1606.06903
Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These cr
Externí odkaz:
http://arxiv.org/abs/1507.01524
Publikováno v:
Annals of Statistics 2012, Vol. 40, No. 1, 294-321
We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to
Externí odkaz:
http://arxiv.org/abs/1104.5617
Autor:
Kalisch, Markus, Meier, Lukas
Dieses Open-Access-Buch gibt eine anwendungsorientierte Einführung in die logistische Regression. Ausgehend von Grundkenntnissen der linearen Regression wird diese zuerst als zweistufiges Modell interpretiert, was den Übergang zur logistischen Regr
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/50435
Publikováno v:
Biometrika 2010, Vol. 97, No. 2, 261-278
We consider variable selection in high-dimensional linear models where the number of covariates greatly exceeds the sample size. We introduce the new concept of partial faithfulness and use it to infer associations between the covariates and the resp
Externí odkaz:
http://arxiv.org/abs/0906.3204
Large contingency tables summarizing categorical variables arise in many areas. For example in biology when a large number of biomarkers are cross-tabulated according to their discrete expression level. Interactions of the variables are generally stu
Externí odkaz:
http://arxiv.org/abs/0904.1510