Iterative regularization methods for ill-posed generalized linear models

Aktivität: VorträgeVortragScience to Science


The problem of regularized maximum-likelihood estimation in ill-posed generalized linear models is studied. Ill-posedness is assumed to be the byproduct of a low-dimensional latent factor model. We provide a class of iterative algorithms extending known penalization/projection techniques and obtain theoretical guarantees under regularity assumptions on the latent model. In particular, when the number of features and observations are both large, we propose a novel iteratively-reweighted-partial-least-squares algorithm outperforming its competitors in both computational efficiency and minimum-norm maximum-likelihood estimation. Our findings are confirmed by simulation studies on both real and generated data.
Zeitraum18 Dez. 2022
EreignistitelCMStatistics 2022: 15th International Conference of the ERCIM WG on Computational and Methodological Statistics
OrtLondon, Großbritannien / Vereinigtes KönigreichAuf Karte anzeigen


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