Zobrazeno 1 - 10
of 1 606
pro vyhledávání: '"Hens, Niel"'
Upholding data privacy especially in medical research has become tantamount to facing difficulties in accessing individual-level patient data. Estimating mixed effects binary logistic regression models involving data from multiple data providers like
Externí odkaz:
http://arxiv.org/abs/2411.04002
We study algorithms to analyze a particular class of Markov population processes that is often used in epidemiology. More specifically, Markov binomial chains are the model that arises from stochastic time-discretizations of classical compartmental m
Externí odkaz:
http://arxiv.org/abs/2408.04902
In medical research, individual-level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as linear mixed models, whic
Externí odkaz:
http://arxiv.org/abs/2407.20796
Kriging is an established methodology for predicting spatial data in geostatistics. Current kriging techniques can handle linear dependencies on spatially referenced covariates. Although splines have shown promise in capturing nonlinear dependencies
Externí odkaz:
http://arxiv.org/abs/2407.05854
Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or imp
Externí odkaz:
http://arxiv.org/abs/2406.00804
Autor:
Parciak, Marcel, Weytjens, Sebastiaan, Hens, Niel, Neven, Frank, Peeters, Liesbet M., Vansummeren, Stijn
Approximate functional dependencies (AFDs) are functional dependencies (FDs) that "almost" hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is
Externí odkaz:
http://arxiv.org/abs/2312.06296
Publikováno v:
Journal of the American Statistical Association, 118(544), 2235-2238 (2023)
This rejoinder responds to discussions by of Caimo, Niezink, and Schweinberger and Fritz of ''A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks'' by Krivitsky, Coletti, and Hens, all published in the
Externí odkaz:
http://arxiv.org/abs/2312.06028
Autor:
Cimpean, Alexandra, Verstraeten, Timothy, Willem, Lander, Hens, Niel, Nowé, Ann, Libin, Pieter
Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models are computationally intensive, it is pivotal to identify optimal strate
Externí odkaz:
http://arxiv.org/abs/2301.12822
Autor:
Reymond, Mathieu, Hayes, Conor F., Willem, Lander, Rădulescu, Roxana, Abrams, Steven, Roijers, Diederik M., Howley, Enda, Mannion, Patrick, Hens, Niel, Nowé, Ann, Libin, Pieter
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strat
Externí odkaz:
http://arxiv.org/abs/2204.05027