Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Hajo Holzmann"'
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
Abstract Machine learning and artificial intelligence have entered biomedical decision-making for diagnostics, prognostics, or therapy recommendations. However, these methods need to be interpreted with care because of the severe consequences for pat
Externí odkaz:
https://doaj.org/article/0262a8233cc641d89dc68d3469433677
Publikováno v:
IEEE Transactions on Information Theory. 68:4182-4200
Publikováno v:
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
Machine learning and artificial intelligence have entered biomedical decision-making for diagnostics, prognostics, or therapy recommendations. However, these methods need to be interpreted with care because of the severe consequences for patients. In
Autor:
Hajo Holzmann, Ann-Kristin Becker
Publikováno v:
IEEE Transactions on Information Theory. 65:4335-4344
We show nonparametric identification of the parameters in the dynamic stochastic block model as recently introduced by Matias and Miele (2017) in the case of binary, finitely weighted, and general edge states. We formulate conditions on the true para
Autor:
Alexander Meister, Hajo Holzmann
Publikováno v:
Bernoulli 26, no. 4 (2020), 2790-2814
Random coefficient regression models are a popular tool for analyzing unobserved heterogeneity, and have seen renewed interest in the recent econometric literature. In this paper we obtain the optimal pointwise convergence rate for estimating the den
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b586b7e47f1a4d02f559fc04ff00290d
https://projecteuclid.org/euclid.bj/1598493631
https://projecteuclid.org/euclid.bj/1598493631
Publikováno v:
PLoS ONE, Vol 8, Iss 3, p e57624 (2013)
We examine the joint distribution of levels of income per capita, life expectancy, and years of schooling across countries in 1960 and in 2000. In 1960 countries were clustered in two groups; a rich, highly educated, high longevity "developed" group
Externí odkaz:
https://doaj.org/article/513e1e2d45d841a6a86152fa784dd524
In various applications of regression analysis, in addition to errors in the dependent observations also errors in the predictor variables play a substantial role and need to be incorporated in the statistical modeling process. In this paper we consi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d988ed8437546ee0bc09d00119b2c47
http://arxiv.org/abs/2009.00936
http://arxiv.org/abs/2009.00936
Autor:
Tobias Zwingmann, Hajo Holzmann
Publikováno v:
Bernoulli 26, no. 1 (2020), 323-351
We show weak convergence of quantile and expectile processes to Gaussian limit processes in the space of bounded functions endowed with an appropriate semimetric which is based on the concepts of epi- and hypo- convergence as introduced in A. Bucher,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4edf50dc63426a43f1c068a74c21e33a
https://projecteuclid.org/euclid.bj/1574758830
https://projecteuclid.org/euclid.bj/1574758830
We construct new testing procedures for spherical and elliptical symmetry based on the characterization that a random vector $X$ with finite mean has a spherical distribution if and only if $\Ex[u^\top X | v^\top X] = 0$ holds for any two perpendicul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4be71fd60c397a337acecce7e57fcc35
Publikováno v:
Electron. J. Statist. 14, no. 1 (2020), 1816-1871
We investigate a flexible two-component semiparametric mixture of regressions model, in which one of the conditional component distributions of the response given the covariate is unknown but assumed symmetric about a location parameter, while the ot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4152c0815bc82ff2c8be5caba1e8c8e
https://projecteuclid.org/euclid.ejs/1587693633
https://projecteuclid.org/euclid.ejs/1587693633