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Consistency of ℓ1 penalized negative binomial regressions

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  • Xie, Fang
  • Xiao, Zhijie

Abstract

We prove the consistency of the ℓ1 penalized negative binomial regression (NBR). A real data application about German health care demand shows that the ℓ1 penalized NBR produces a more concise but more accurate model, comparing to the classical NBR.

Suggested Citation

  • Xie, Fang & Xiao, Zhijie, 2020. "Consistency of ℓ1 penalized negative binomial regressions," Statistics & Probability Letters, Elsevier, vol. 165(C).
    • Handle: RePEc:eee:stapro:v:165:y:2020:i:c:s016771522030119x
    DOI: 10.1016/j.spl.2020.108816
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    References listed on IDEAS

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    1. William H. Aeberhard & Eva Cantoni & Stephane Heritier, 2014. "Robust inference in the negative binomial regression model with an application to falls data," Biometrics, The International Biometric Society, vol. 70(4), pages 920-931, December.
    2. Gu Mi & Yanming Di & Daniel W Schafer, 2015. "Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.
    3. Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
    4. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
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