On non-asymptotic bounds for estimation in generalized linear models with highly correlated design
Abstract
We study a high-dimensional generalized linear model and penalized empirical risk minimization with ℓ 1 penalty. Our aim is to provide a non-trivial illustration that non-asymptotic bounds for the estimator can be obtained without relying on the chaining technique and/or the peeling device.
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publishedJournal / series
Research Report / Seminar für Statistik, Eidgenössische Technische Hochschule (ETH)Volume
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Seminar für Statistik, Eidgenössische Technische Hochschule (ETH)Subject
convex hull; convex loss; covering number; non-asymptotic bound; penalized M-estimationOrganisational unit
03717 - van de Geer, Sara (emeritus) / van de Geer, Sara (emeritus)
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Is previous version of: http://hdl.handle.net/20.500.11850/4208
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