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pro vyhledávání: '"Akhavan Masouleh, Sepehr"'
We are interested in survival analysis of hemodialysis patients for whom several biomarkers are recorded over time. Motivated by this challenging problem, we propose a general framework for multivariate joint longitudinal-survival modeling that can b
Externí odkaz:
http://arxiv.org/abs/1807.09969
By establishing a connection between bi-directional Helmholtz machines and information theory, we propose a generalized Helmholtz machine. Theoretical and experimental results show that given \textit{shallow} architectures, the generalized model outp
Externí odkaz:
http://arxiv.org/abs/1807.06054
Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predic
Externí odkaz:
http://arxiv.org/abs/1712.03483
Autor:
Akhavan Masouleh, Sepehr
Publikováno v:
Akhavan Masouleh, Sepehr. (2016). A New Class of Bayesian Semi-Parametric Joint Longitudinal-Survival Models for Biomarker Discovery. UC Irvine: Statistics. Retrieved from: http://www.escholarship.org/uc/item/45z8c9jd
In studying the progression of a disease and to better predict time to death (survival data), investigators often collect repeated measures over time (longitudinal data) and are interested in testing the association between risk factors, including co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::578531897942c1c5f168224e722592c5
http://www.escholarship.org/uc/item/45z8c9jd
http://www.escholarship.org/uc/item/45z8c9jd