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pro vyhledávání: '"A. B�hlmann"'
Contribution to the discussion of the paper "Causal inference using invariant prediction: identification and confidence intervals" by Peters, B��hlmann and Meinshausen, to appear in the Journal of the Royal Statistical Society, Series B.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2df56af51182c3e26a98792cbf695c1
Autor:
Helmut Duddeck
Publikováno v:
Magnetic Resonance in Chemistry. 40:247-247
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f629d38a41a6512295be03735600338
https://doi.org/10.1002/mrc.960
https://doi.org/10.1002/mrc.960
Publikováno v:
Klinische Wochenschrift. 51:994-998
Bei 220 Patienten mit unterschiedlichen Lungenfunktionsstorungen wird die Beziehung zwischen der „steady-state“-CO-Diffusionskapazitat in Ruhe nach Filley und der Blutgasanalyse im Arbeitsversuch zum Nachweis von Diffusionsstorungen untersucht. D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10e70aea34cd3abd2ff56bd5d08215e9
https://doi.org/10.1007/bf01468290
https://doi.org/10.1007/bf01468290
Autor:
Renaux, Claude, B��hlmann, Peter
Hierarchical inference in (generalized) regression problems is powerful for finding significant groups or even single covariates, especially in high-dimensional settings where identifiability of the entire regression parameter vector may be ill-posed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82ece49720c9c21dfc2b4383ed65559d
http://arxiv.org/abs/2104.15028
http://arxiv.org/abs/2104.15028
We consider estimation of undirected Gaussian graphical models and inverse covariances in high-dimensional scenarios by penalizing the corresponding precision matrix. While single $L_1$ (Graphical Lasso) and $L_2$ (Graphical Ridge) penalties for the
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::043a6a74adf7e584535f3bd31b47f1bd
http://arxiv.org/abs/2101.02148
http://arxiv.org/abs/2101.02148
Publikováno v:
Electronic Journal of Statistics, 15 (2)
High-dimensional group inference is an essential part of statistical methods for analysing complex data sets, including hierarchical testing, tests of interaction, detection of heterogeneous treatment effects and inference for local heritability. Gro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ea7f0d9162cdb76917a935a2c736a0e
https://hdl.handle.net/20.500.11850/525120
https://hdl.handle.net/20.500.11850/525120
Autor:
B��hlmann, Peter, ��evid, Domagoj
We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95f3ee45f3dfad13999d6ef0def8ff3b
http://arxiv.org/abs/2008.06234
http://arxiv.org/abs/2008.06234
We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or directed against testing specific non-linearities and interaction eff
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52e66898de0be810691cd400f06ddf8c
http://arxiv.org/abs/1908.03606
http://arxiv.org/abs/1908.03606
We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values. We advocate three imputation like methods and investigate their implications on common losses use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::acf39821bdf0d47664a718ba3531925e
http://arxiv.org/abs/1907.05409
http://arxiv.org/abs/1907.05409
We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5e21b9af514e6ba5fae58ddce1c263a
http://arxiv.org/abs/1801.06229
http://arxiv.org/abs/1801.06229