Generalized linear models with structured sparsity estimators

Mehmet Caner

Journal of Econometrics, 2023, vol. 236, issue 2

Abstract: In this paper, we introduce structured sparsity estimators for use in Generalized Linear Models. Structured sparsity estimators in the least squares loss are introduced by Stucky and van de Geer (2018). Their proofs exclusively depend on their use of fixed design and normal errors. We extend their results to debiased structured sparsity estimators with Generalized Linear Model based loss through incorporating random design and non-sub Gaussian data. Structured sparsity estimation means that penalized loss functions with a possible sparsity structure in a norm. These norms include norms generated from convex cones.

Keywords: Uniformity; Size and power of the test; Restrictions (search for similar items in EconPapers)
JEL-codes: C18 C21 C55 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Generalized Linear Models with Structured Sparsity Estimators (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:236:y:2023:i:2:s030440762300194x

DOI: 10.1016/j.jeconom.2023.105478

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