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pro vyhledávání: '"van de Wiel Mark A"'
Machine learning models often face challenges in medical applications due to covariate shifts, where discrepancies between training and target data distributions can decrease predictive accuracy. This paper introduces an adaptation of Classification
Externí odkaz:
http://arxiv.org/abs/2410.20978
Combining data from various sources empowers researchers to explore innovative questions, for example those raised by conducting healthcare monitoring studies. However, the lack of a unique identifier often poses challenges. Record linkage procedures
Externí odkaz:
http://arxiv.org/abs/2407.06835
The regression discontinuity design (RDD) is a quasi-experimental approach used to estimate the causal effects of an intervention assigned based on a cutoff criterion. RDD exploits the idea that close to the cutoff units below and above are similar;
Externí odkaz:
http://arxiv.org/abs/2406.11585
The high dimensional nature of genomics data complicates feature selection, in particular in low sample size studies - not uncommon in clinical prediction settings. It is widely recognized that complementary data on the features, `co-data', may impro
Externí odkaz:
http://arxiv.org/abs/2405.04917
Medical prediction applications often need to deal with small sample sizes compared to the number of covariates. Such data pose problems for prediction and variable selection, especially when the covariate-response relationship is complicated. To add
Externí odkaz:
http://arxiv.org/abs/2311.09997
In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Correlation Analysis (CCA), integrate two (or more) datasets to discover
Externí odkaz:
http://arxiv.org/abs/2310.02169
We address a classical problem in statistics: adding two-way interaction terms to a regression model. As the covariate dimension increases quadratically, we develop an estimator that adapts well to this increase, while providing accurate estimates an
Externí odkaz:
http://arxiv.org/abs/2309.13998
Autor:
Busatto, Claudio, van de Wiel, Mark
High-dimensional data often arise from clinical genomics research to infer relevant predictors of a particular trait. A way to improve the predictive performance is to include information on the predictors derived from prior knowledge or previous stu
Externí odkaz:
http://arxiv.org/abs/2303.05898
Autor:
van de Wiel, Mark A., Leday, Gwenaël G. R., Hoogland, Jeroen, Heymans, Martijn W., van Zwet, Erik W., Zwinderman, Ailko H.
While shrinkage is essential in high-dimensional settings, its use for low-dimensional regression-based prediction has been debated. It reduces variance, often leading to improved prediction accuracy. However, it also inevitably introduces bias, whic
Externí odkaz:
http://arxiv.org/abs/2301.09890
In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight in
Externí odkaz:
http://arxiv.org/abs/2212.08581