On Bias Correction in a Class of Inflated Beta Regression Models

Raydonal Ospina, Silvia L. P. Ferrari

Abstract


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.

Full Text: PDF DOI: 10.5539/ijsp.v1n2p269

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International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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