Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Hudson, Aaron"'
Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. Th
Externí odkaz:
http://arxiv.org/abs/2410.00985
This manuscript explores the intersection of surrogate outcomes and adaptive designs in statistical research. While surrogate outcomes have long been studied for their potential to substitute long-term primary outcomes, current surrogate evaluation m
Externí odkaz:
http://arxiv.org/abs/2408.02667
Autor:
Hudson, Aaron, Geng, Elvin H., Odeny, Thomas A., Bukusi, Elizabeth A., Petersen, Maya L., van der Laan, Mark J.
The causal dose response curve is commonly selected as the statistical parameter of interest in studies where the goal is to understand the effect of a continuous exposure on an outcome.Most of the available methodology for statistical inference on t
Externí odkaz:
http://arxiv.org/abs/2306.07736
Autor:
Hudson, Aaron
It is often of interest to assess whether a function-valued statistical parameter, such as a density function or a mean regression function, is equal to any function in a class of candidate null parameters. This can be framed as a statistical inferen
Externí odkaz:
http://arxiv.org/abs/2306.07492
It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimands can typically only be estimated at a slower-than-parametric rate in
Externí odkaz:
http://arxiv.org/abs/2105.06646
Autor:
Hudson, Aaron, Shojaie, Ali
Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present
Externí odkaz:
http://arxiv.org/abs/2010.08703
Autor:
Hudson, Aaron, Shojaie, Ali
Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result,
Externí odkaz:
http://arxiv.org/abs/2010.08704
Publikováno v:
PLoS ONE; 9/6/2024, Vol. 19 Issue 9, p1-17, 17p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.