ArticleJournal of applied statistics2020
Variable selection under multicollinearity using modified log penalty.
Article in Journal of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics.bioRxiv : the preprint server for biology · 2025Article
- Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau.Environmental monitoring and assessment · 2021Article
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2 authors.
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Abstract
To handle the multicollinearity issues in the regression analysis, a class of 'strictly concave penalty function' is described in this paper. As an example, a new penalty function called 'modified log penalty' is introduced. The penalized estimator based on strictly concave penalties enjoys the oracle property under certain regularity conditions discussed in the literature. In the multicollinearity cases where such conditions are not applicable, the behaviors of the strictly concave penalties are discussed through examples involving strongly correlated covariates. Real data examples and simulation studies are provided to show the finite-sample performance of the modified log penalty in terms of prediction error under scenarios exhibiting multicollinearity.
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