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pro vyhledávání: '"Rossell, David"'
Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of non-linear effects and model misspecification. Specifically, for common semi-parametric me
Externí odkaz:
http://arxiv.org/abs/2401.10235
Autor:
Rossell, David
I briefly discuss the Martingale Posteriors Distributions paper by Edwing Hong, Chris Holmes and Stephen G. Walker
Externí odkaz:
http://arxiv.org/abs/2303.02403
We consider two applications where we study how dependence structure between many variables is linked to external network data. We first study the interplay between social media connectedness and the co-evolution of the COVID-19 pandemic across USA c
Externí odkaz:
http://arxiv.org/abs/2210.11107
Autor:
Semken, Christoph, Rossell, David
A key issue in science is assessing robustness to data analysis choices, while avoiding selective reporting and providing valid inference. Specification Curve Analysis is a tool intended to prevent selective reporting. Alas, when used for inference i
Externí odkaz:
http://arxiv.org/abs/2201.05381
We address modelling and computational issues for multiple treatment effect inference under many potential confounders. Our main contribution is providing a trade-off between preventing the omission of relevant confounders, while not running into an
Externí odkaz:
http://arxiv.org/abs/2110.00314
Autor:
Jewson, Jack, Rossell, David
Statisticians often face the choice between using probability models or a paradigm defined by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into a proper probability model, there are many tools to decide which
Externí odkaz:
http://arxiv.org/abs/2106.01214
Standard likelihood penalties to learn Gaussian graphical models are based on regularising the off-diagonal entries of the precision matrix. Such methods, and their Bayesian counterparts, are not invariant to scalar multiplication of the variables, u
Externí odkaz:
http://arxiv.org/abs/2104.10099
We propose the approximate Laplace approximation (ALA) to evaluate integrated likelihoods, a bottleneck in Bayesian model selection. The Laplace approximation (LA) is a popular tool that speeds up such computation and equips strong model selection pr
Externí odkaz:
http://arxiv.org/abs/2012.07429
Autor:
Rossell, David, Zwiernik, Piotr
The Gaussian model equips strong properties that facilitate studying and interpreting graphical models. Specifically it reduces conditional independence and the study of positive association to determining partial correlations and their signs. When G
Externí odkaz:
http://arxiv.org/abs/2004.13779
We discuss the role of misspecification and censoring on Bayesian model selection in the contexts of right-censored survival and concave log-likelihood regression. Misspecification includes wrongly assuming the censoring mechanism to be non-informati
Externí odkaz:
http://arxiv.org/abs/1907.13563