Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Marković, Jelena P."'
Autor:
Davkova Iskra, Zhivikj Zoran, Kukić-Marković Jelena, Cvetkovik-Karanfilova Ivana, Stefkov Gjoshe, Kulevanova Svetlana, Karapandzova Marija
Publikováno v:
Arhiv za farmaciju, Vol 74, Iss 3, Pp 298-315 (2024)
Treating overweight and obesity with medications generally offers initial advantages but can result in weight regain after stopping the drugs, as well as in medication-related side effects, and the potential for substance misuse. The allure of herbal
Externí odkaz:
https://doaj.org/article/e14e1a1232724aae98829891b7b6811a
We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection procedure. The conditional correction for selection requires knowledg
Externí odkaz:
http://arxiv.org/abs/1901.09973
Autor:
Kukić-Marković Jelena
Publikováno v:
Arhiv za farmaciju, Vol 73, Iss 4, Pp 318-335 (2023)
Plants are rich sources of secondary metabolites that exhibit diverse biological and pharmacological effects. Some plant ingredients, primarily phenolics, have significant in vitro antioxidant activity, which implies their contribution to the mainten
Externí odkaz:
https://doaj.org/article/5ac486f29b6e4e9d9a685c0460181868
Autor:
Markovic, Jelena, Sepehri, Amir
This work introduces a class of rejection-free Markov chain Monte Carlo (MCMC) samplers, named the Bouncy Hybrid Sampler, which unifies several existing methods from the literature. Examples include the Bouncy Particle Sampler of Peters and de With (
Externí odkaz:
http://arxiv.org/abs/1802.04366
Investigators often use the data to generate interesting hypotheses and then perform inference for the generated hypotheses. P-values and confidence intervals must account for this explorative data analysis. A fruitful method for doing so is to condi
Externí odkaz:
http://arxiv.org/abs/1801.09037
Autor:
Sepehri, Amir, Markovic, Jelena
In this work we present a non-reversible, tuning- and rejection-free Markov chain Monte Carlo which naturally fits in the framework of hit-and-run. The sampler only requires access to the gradient of the log-density function, hence the normalizing co
Externí odkaz:
http://arxiv.org/abs/1711.07177
We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory (roughly speaking "model free") and confirm
Externí odkaz:
http://arxiv.org/abs/1707.06692
We develop tools to do valid post-selective inference for a family of model selection procedures, including choosing a model via cross-validated Lasso. The tools apply universally when the following random vectors are jointly asymptotically multivari
Externí odkaz:
http://arxiv.org/abs/1703.06559
We develop a Monte Carlo-free approach to inference post output from randomized algorithms with a convex loss and a convex penalty. The pivotal statistic based on a truncated law, called the selective pivot, usually lacks closed form expressions. Inf
Externí odkaz:
http://arxiv.org/abs/1703.06154
Autor:
Markovic, Jelena, Taylor, Jonathan
In this work, we provide a refinement of the selective CLT result of Tian and Taylor (2015), which allows for selective inference in non-parametric settings by adjusting for the asymptotic Gaussian limit for selection. Under some regularity assumptio
Externí odkaz:
http://arxiv.org/abs/1612.07811