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Title: Consistent Estimation of Generalized Linear Models with High Dimensional Predictors via Stepwise Regression
Predictive models play a central role in decision making. Penalized regression approaches, such as least absolute shrinkage and selection operator (LASSO), have been widely used to construct predictive models and explain the impacts of the selected predictors, but the estimates are typically biased. Moreover, when data are ultrahigh-dimensional, penalized regression is usable only after applying variable screening methods to downsize variables. We propose a stepwise procedure for fitting generalized linear models with ultrahigh dimensional predictors. Our procedure can provide a final model; control both false negatives and false positives; and yield consistent estimates, which are useful to gauge the actual effect size of risk factors. Simulations and applications to two clinical studies verify the utility of the method.  more » « less
Award ID(s):
1915099
PAR ID:
10249956
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Entropy
Volume:
22
Issue:
9
ISSN:
1099-4300
Page Range / eLocation ID:
965
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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