# Replica analysis of overfitting in generalized linear models

@article{Coolen2020ReplicaAO, title={Replica analysis of overfitting in generalized linear models}, author={A C C Coolen and Mansoor Sheikh and Alexander Mozeika and Fabi{\'a}n Aguirre-L{\'o}pez and Fabrizio Antenucci}, journal={arXiv: Disordered Systems and Neural Networks}, year={2020} }

Nearly all statistical inference methods were developed for the regime where the number $N$ of data samples is much larger than the data dimension $p$. Inference protocols such as maximum likelihood (ML) or maximum a posteriori probability (MAP) are unreliable if $p=O(N)$, due to overfitting. This limitation has for many disciplines with increasingly high-dimensional data become a serious bottleneck. We recently showed that in Cox regression for time-to-event data the overfitting errors are not…

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