{"id":503,"date":"2021-10-18T13:39:06","date_gmt":"2021-10-18T13:39:06","guid":{"rendered":"https:\/\/website.prod.unilu.spikeseed.cloud\/fstm-fr\/news\/what-make-statistics-robust\/"},"modified":"2021-10-18T13:39:06","modified_gmt":"2021-10-18T13:39:06","slug":"what-make-statistics-robust","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/fstm-fr\/news\/what-make-statistics-robust\/","title":{"rendered":"What make statistics robust?"},"content":{"rendered":"

Robustness has become a major issue in statistical learning and deep learning today. Mathematician Yannick Baraud from the University of Luxembourg has recently published a paper in the prestigious journal\u00a0Probability Theory and Related Fields to tackle the problem of robustness in statistics.<\/p>

It is well known that classical statistical procedures do not offer the reliability that scientists are looking for when trained on a dataset that contains a small fraction of \u00ab\u00a0bad data\u00a0\u00bb. This problem has consequences for the ability of new algorithms to classify images and make good predictions\u00a0from real data set that may be partly of poor quality.\u00a0<\/p>

The paper \u00ab\u00a0Tests and estimation strategies associated to some loss functions<\/a>\u00a0\u00bb written by\u00a0Yannick Baraud<\/a>, Professor in statistics within the Department of Mathematics at the University of Luxembourg, provides a general mathematical solution to this problem and shows how it is possible to design a statistical procedure capable of estimating correctly from data, even when the dataset is contaminated by undesirable data.<\/p>

Probability Theory and Related Fields is one of the most prestigious journals in the field of probability and mathematical statistics.\u00a0<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"

Robustness has become a major issue in statistical learning and deep learning today. 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