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Zero-adjusted beta prime regression model
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DOI：10.1007/s10182-026-00558-5.png)
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Abstract
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There are practical situations in which continuous data on the positive real line contain an excess of zeros. In this context, building on the flexibility of the beta prime (BP) distribution, we introduce the zero-adjusted BP distribution that accommodate the presence of zeros and propose zero-adjusted BP regression to deal with the issue of regression estimation when there are data with zeros in the dependent variable. The maximum likelihood method is used to estimate the model parameters. Also, we consider residual analysis. Simulation studies are conducted to evaluate its finite sample performance. Finally, a real dataset from a population-based study of fumonisin production by Fusarium verticillioides in corn grains, conducted by the Institute of Biomedical Sciences at the University of S & atilde;o Paulo, is discussed in detail.
Keywords:
Beta Prime distribution
Maximum likelihood estimates
Positive real data
Regression model
Zero adjusted
Journal
A
IF:
1.4
Papers:
24
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0
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