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Variable selection approach for zero-inflated count data via adaptive lasso

Author

Listed:
  • Ping Zeng
  • Yongyue Wei
  • Yang Zhao
  • Jin Liu
  • Liya Liu
  • Ruyang Zhang
  • Jianwei Gou
  • Shuiping Huang
  • Feng Chen

Abstract

This article proposes a variable selection approach for zero-inflated count data analysis based on the adaptive lasso technique. Two models including the zero-inflated Poisson and the zero-inflated negative binomial are investigated. An efficient algorithm is used to minimize the penalized log-likelihood function in an approximate manner. Both the generalized cross-validation and Bayesian information criterion procedures are employed to determine the optimal tuning parameter, and a consistent sandwich formula of standard errors for nonzero estimates is given based on local quadratic approximation. We evaluate the performance of the proposed adaptive lasso approach through extensive simulation studies, and apply it to analyze real-life data about doctor visits.

Suggested Citation

  • Ping Zeng & Yongyue Wei & Yang Zhao & Jin Liu & Liya Liu & Ruyang Zhang & Jianwei Gou & Shuiping Huang & Feng Chen, 2014. "Variable selection approach for zero-inflated count data via adaptive lasso," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 879-894, April.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:879-894
    DOI: 10.1080/02664763.2013.858672
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    References listed on IDEAS

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    Cited by:

    1. Rodolfo Metulini & Roberto Patuelli & Daniel A. Griffith, 2018. "A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade," Econometrics, MDPI, vol. 6(1), pages 1-15, February.

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