Zobrazeno 1 - 10
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pro vyhledávání: '"Daniels, P. J."'
Autor:
Bhandari, Saurabh, Daniels, Michael J., Josefsson, Maria, Lloyd-Jones, Donald M., Siddique, Juned
Causal mediation analysis of observational data is an important tool for investigating the potential causal effects of medications on disease-related risk factors, and on time-to-death (or disease progression) through these risk factors. However, whe
Externí odkaz:
http://arxiv.org/abs/2411.18739
We prove Ilmanen's resolution of point singularities conjecture by establishing short-time smoothness of the level set flow of a smooth hypersurface with isolated conical singularities. This shows how the mean curvature flow evolves through asymptoti
Externí odkaz:
http://arxiv.org/abs/2312.00759
Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulation
Externí odkaz:
http://arxiv.org/abs/2311.00553
In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is
Externí odkaz:
http://arxiv.org/abs/2305.05099
We propose a new Bayesian non-parametric (BNP) method for estimating the causal effects of mediation in the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observ
Externí odkaz:
http://arxiv.org/abs/2305.05017
Autor:
Burns, Natalie, Daniels, Michael J.
Enriched Dirichlet process mixture (EDPM) models are Bayesian nonparametric models which can be used for nonparametric regression and conditional density estimation and which overcome a key disadvantage of jointly modeling the response and predictors
Externí odkaz:
http://arxiv.org/abs/2305.01631
Mediation analysis with contemporaneously observed multiple mediators is an important area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much
Externí odkaz:
http://arxiv.org/abs/2208.13382
Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type
Externí odkaz:
http://arxiv.org/abs/2208.09869
Model development often takes data structure, subject matter considerations, model assumptions, and goodness of fit into consideration. To diagnose issues with any of these factors, it can be helpful to understand regression model estimates at a more
Externí odkaz:
http://arxiv.org/abs/2201.03077
Autor:
Luo, Chuji, Daniels, Michael J.
Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impact the response variable in nonlinear and/or non-additive ways. In this
Externí odkaz:
http://arxiv.org/abs/2112.13998