Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

Saptarshi Chatterjee, Shrabanti Chowdhury, Himel Mallick, Prithish Banerjee, Broti Garai

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Pages (from-to)3012-3026
Number of pages15
JournalStatistics in Medicine
Volume37
Issue number20
DOIs
StatePublished - 10 Sep 2018

Keywords

  • bi-level variable selection
  • group LASSO
  • group bridge
  • group regularization
  • health care demand
  • zero-inflated negative binomial

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