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A beta inflated mean regression model for fractional response variables

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  • Cristian L. Bayes
  • Luis Valdivieso

Abstract

This article proposes a new regression model for a dependent fractional random variable on the interval that takes with positive probability the extreme values 0 or 1. Our model relates the expected value of this variable with a linear predictor through a special parametrization that let the parameters free in the parameter space. A simulation-based study and an application to capital structure choices were conducted to analyze the performance of the likelihood estimators in the model. The results show not only accurate estimations and a better fit than other traditional models but also a more straightforward and clear way to estimate the effects of a set of covariates over the mean of a fractional response.

Suggested Citation

  • Cristian L. Bayes & Luis Valdivieso, 2016. "A beta inflated mean regression model for fractional response variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1814-1830, August.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1814-1830
    DOI: 10.1080/02664763.2015.1120711
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    Cited by:

    1. Luiz M A Lima-Filho & Tarciana Liberal Pereira & Tatiene C Souza & Fábio M Bayer, 2020. "Process monitoring using inflated beta regression control chart," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.

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