The joint lasso:high-dimensional regression for group structured data

Dondelinger, Frank and Mukherjee, Sach (2018) The joint lasso:high-dimensional regression for group structured data. Biostatistics. ISSN 1465-4644

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Abstract

We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where subsets of samples, representing for example disease subtypes, may differ with respect to underlying regression models. In the high-dimensional setting, estimating a different model for each subgroup is challenging due to limited sample sizes. Focusing on the case in which subgroup-specific models may be expected to be similar but not necessarily identical, we treat subgroups as related problem instances and jointly estimate subgroup-specific regression coefficients. This is done in a penalized framework, combining an l1 term with an additional term that penalizes differences between subgroup-specific coefficients. This gives solutions that are globally sparse but that allow information-sharing between the subgroups. We present algorithms for estimation and empirical results on simulated data and using Alzheimer’s disease, amyotrophic lateral sclerosis, and cancer datasets. These examples demonstrate the gains joint estimation can offer in prediction as well as in providing subgroup-specific sparsity patterns.

Item Type: Journal Article
Journal or Publication Title: Biostatistics
Uncontrolled Keywords: /dk/atira/pure/subjectarea/aacsb/disciplinebasedresearch
Subjects:
Departments: Faculty of Health and Medicine > Medicine
ID Code: 127406
Deposited By: ep_importer_pure
Deposited On: 11 Sep 2018 10:42
Refereed?: Yes
Published?: Published
Last Modified: 05 Dec 2019 04:55
URI: https://eprints.lancs.ac.uk/id/eprint/127406

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