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• W2807887605 abstract "In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels. Motivated by the widespread availability of zero-inflated count outcomes and grouped covariates in many practical applications, we consider group regularization for zero-inflated negative binomial regression models. Using a least squares approximation of the mixture likelihood and a variety of group-wise penalties on the coefficients, we propose a unified algorithm (Gooogle: Group Regularization for Zero-inflated Count Regression Models) to efficiently compute the entire regularization path of the estimators. We investigate the finite sample performance of these methods through extensive simulation experiments and the analysis of a German health care demand dataset. Finally, we derive theoretical properties of these methods under reasonable assumptions, which further provides deeper insight into the asymptotic behavior of these approaches. The open source software implementation of this method is publicly available at: https://github.com/himelmallick/Gooogle." @default.
• W2807887605 created "2018-06-21" @default.
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• W2807887605 date "2018-06-14" @default.
• W2807887605 modified "2023-09-25" @default.
• W2807887605 title "Group regularization for zero‐inflated negative binomial regression models with an application to health care demand in Germany" @default.
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• W2807887605 doi "https://doi.org/10.1002/sim.7804" @default.
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