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SMLE: An R Package for Joint Feature Screening in Ultrahigh-Dimensional GLMs
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DOI：10.18637/jss.v115.i08.png)
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Abstract
En 中文
Sparsity-restricted maximum likelihood estimation (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features in the screening process. We develop a publicly available R package SMLE, which provides a user-friendly environment to carry out the SMLE method in generalized linear models. In particular, the package includes functions to conduct SMLE-screening and the related post-screening selection with popular selection criteria such as AIC and (extended) BIC. The package gives users the flexibility in controlling a series of screening parameters and accommodates both numerical and categorical feature input. The usage of SMLE is illustrated on extensive numerical examples, where the promising performance of the package is well observed.
Keywords:
EBIC
generalized linear models
iterative hard-thresholding
joint feature screen-ing
ultrahigh-dimensional data
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8.1
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616
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4.6W
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