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Quasi-binomial zero-inflated regression model suitable for variables with bounded support.

E Gómez-Déniz1, D I Gallardo2, H W Gómez3

  • 1Department of Quantitative Methods in Economics and TiDES Institute, University of Las Palmas de Gran Canaria, Las Palmas, Spain.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel zero-inflated regression model using the quasi-binomial distribution for bounded data. It offers a valuable alternative for analyzing count data with excess zeros, outperforming existing models in specific scenarios.

Keywords:
62A0562F03Fitquasi binomial distributionscore testzero-inflated

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Regression models are crucial for analyzing dependent variables with exogenous covariates.
  • Existing models like Poisson, negative binomial, and generalized Poisson, along with zero-inflated and hurdle versions, are widely used.
  • The quasi-binomial distribution, despite its potential for bounded data, has been largely overlooked in regression modeling.

Purpose of the Study:

  • To address the research gap concerning the quasi-binomial distribution in regression analysis.
  • To propose and evaluate a zero-inflated regression model based on the quasi-binomial distribution.
  • To compare the proposed model against the zero-inflated binomial distribution and a homogeneous model.

Main Methods:

  • Development of a zero-inflated regression model utilizing the quasi-binomial distribution.
  • Estimation of model parameters using moments and maximum likelihood estimators.
  • Implementation of a score test for comparing the quasi-binomial model with the binomial and homogeneous models.

Main Results:

  • The proposed zero-inflated quasi-binomial regression model is presented.
  • The score test provides a method for model comparison.
  • The analysis is demonstrated using two well-known statistical datasets characterized by a high proportion of zero counts.

Conclusions:

  • The zero-inflated quasi-binomial distribution offers a viable alternative for regression modeling, particularly for variables with bounded support and excess zeros.
  • The developed methodology allows for robust comparison with existing models.
  • This research highlights the underutilization and potential of the quasi-binomial distribution in statistical modeling.