Variable selection for sparse logistic regression

Zanhua Yin ()
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Zanhua Yin: Gannan Normal University

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 7, No 4, 836 pages

Abstract: Abstract We consider the variable selection problem in a sparse logistical regression model. Inspired by the square-root Lasso, we develop a weighted score Lasso for logistical regression. The new method yields the estimation $${\ell }_1$$ ℓ 1 error bound under similar assumptions as introduced in Bach et al. (Electron J Stat 4:384–414, 2010). Compared to standard Lasso, the weighted score Lasso provides a direct choice for the tuning parameter. Both theoretical and simulation results confirm the satisfactory performance of the proposed method. We illustrate our methodology with a real microarray data set.

Keywords: Score function; High dimensions; Lasso; Logistic regression model; Sparse (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00184-020-00764-4

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