Robust VIF Regression with Application to Variable Selection in Large Datasets

Dans le cadre du Programme santé environ‑ nement-santé travail financé par l’Agence natio‑ nale de la recherche, une étude a été menée pour estimer les fractions de cas

Renal transplant recipients with abnormally low bone den- sity may have a low fracture rate; more than one-third of the patients with fractures showed no signs of osteoporosis in

Finally, let us mention that the proof of this main result relies on an upper bound for the expectation of the supremum of an empirical process over a VC-subgraph class.. This

Most of the methods that have been proposed for variable selection in linear regression deal with the case where the covariates are assumed to be nonrandom; for this case,

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Three main results are provided: optimal bounds for the estimation prob- lem in Section 3, that improve in particular previous results obtained with LASSO penalization [23],

We conducted a series of simulation studies to investigate the performance of our proposed variable selection approach on continuous, binary and count response out- comes using