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

Research output: Contribution to journalJournal articlepeer-review

  • The Alzheimer's Disease Neuroimaging Initiative
<mark>Journal publication date</mark>1/04/2020
Issue number2
Number of pages17
Pages (from-to)219–235
Publication StatusPublished
Early online date5/09/18
<mark>Original language</mark>English


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.