On Bias Correction in a Class of Inflated Beta Regression Models

  •  Raydonal Ospina    
  •  Silvia Ferrari    


Inflated beta regression models bear practical applicability in modeling rates and proportions measured continuously in the presence of zeros and/or ones. In this article, the second-order bias of maximum likelihood estimators for zero-or-one inflated beta regression model parameters is derived. This enables one to obtain corrected estimators that are approximately unbiased. Numerical results exhibit that corrected estimators show better performance in terms of mean-square error and bias when compared to maximum likelihood estimators.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-7032
  • ISSN(Online): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

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