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Related Experiment Video

Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Zero-inflated Bell regression models for count data.

Artur J Lemonte1, Germán Moreno-Arenas2, Fredy Castellares3

  • 1Departamento de Estatística, CCET, Universidade Federal do Rio Grande do Norte, Natal/RN, Brazil.

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

A new zero-inflated Bell regression model offers a simple, effective alternative for analyzing count data. This statistical model demonstrates strong performance in parameter estimation and assessing model assumptions, proving useful in practical applications.

Keywords:
62F1062J0562J20Bell distributioncount dataexcess zerosoverdispersionzero-inflated models

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Area of Science:

  • Statistics
  • Biostatistics

Background:

  • Zero-inflated models are commonly used for count data with excess zeros.
  • Existing models may not always capture the underlying data structure effectively.

Purpose of the Study:

  • To introduce a novel zero-inflated Bell regression model for count data.
  • To provide a simple yet effective alternative to existing zero-inflated regression models.
  • To assess the model's performance and utility in practical scenarios.

Main Methods:

  • Development of the zero-inflated Bell family of distributions.
  • Application of the maximum likelihood method for parameter estimation.
  • Utilizing Pearson residuals, global, and local influence methods for model diagnostics.

Main Results:

  • The maximum likelihood method is effective for estimating zero-inflated Bell regression parameters.
  • The proposed model demonstrates suitability for count data, including an application to infected blood cell counts.
  • The new model shows better appropriateness for the considered count data compared to familiar alternatives.

Conclusions:

  • The zero-inflated Bell regression model is a valuable addition to the statistical toolkit for count data analysis.
  • The model provides a robust framework for inference and diagnostics.
  • Its practical application highlights its potential for real-world data analysis in various fields.