Bootstrapping Lasso in Generalized Linear Models Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2403.19515
Generalized linear models or GLM constitute plethora of sub-models which extends the ordinary linear regression by connecting the mean of response variable with the covariates through appropriate link functions. On the other hand, Lasso is a popular and easy-to-implement penalization method in regression when not all covariates are relevant. However, Lasso does not generally have a tractable asymptotic distribution (Knight and Fu (2000)). In this paper, we develop a Bootstrap method which works as an alternative to the asymptotic distribution of Lasso for all the submodels of GLM. We support our theoretical findings by showing good finite-sample properties of the proposed Bootstrap method through a moderately large simulation study. We also implement our method on a real data set.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.19515
- https://arxiv.org/pdf/2403.19515
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393336148