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A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression

Author

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

Abstract

We consider the problem of modelling count data with excess zeros using Zero-Inflated Poisson (ZIP) regression. Recently, various regularization methods have been developed for variable selection in ZIP models. Among these, EM LASSO is a popular method for simultaneous variable selection and parameter estimation. However, EM LASSO suffers from estimation inefficiency and selection inconsistency. To remedy these problems, we propose a set of EM adaptive LASSO methods using a variety of data-adaptive weights. We show theoretically that the new methods are able to identify the true model consistently, and the resulting estimators can be as efficient as oracle. The methods are further evaluated through extensive synthetic experiments and applied to a German health care demand dataset.

Suggested Citation

  • Prithish Banerjee & Broti Garai & Himel Mallick & Shrabanti Chowdhury & Saptarshi Chatterjee, 2018. "A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression," Journal of Probability and Statistics, Hindawi, vol. 2018, pages 1-9, December.
    • Handle: RePEc:hin:jnljps:2834183
    DOI: 10.1155/2018/2834183
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