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Testing inflated zeros in binomial regression models.

Peng Ye1,2, Yi Tang3, Liuquan Sun4

  • 1School of Statistics, University of International Business and Economics, Beijing, P. R. China.

Biometrical Journal. Biometrische Zeitschrift
|September 23, 2020
PubMed
Summary
This summary is machine-generated.

A new statistical test effectively identifies excessive zeros in binomial regression models, crucial for selecting appropriate zero-inflated binomial models. This method improves accuracy and avoids issues with standard models.

Keywords:
binomial regression modelpowerscore testtype I errorzero-inflated binomial (ZIB) regression model

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Binomial regression models are standard for proportion data (e.g., disease mortality, infection rates).
  • Excessive zeros in data can bias standard binomial models, necessitating alternative approaches like zero-inflated binomial (ZIB) models.
  • Choosing between binomial and ZIB models requires reliably detecting zero inflation.

Purpose of the Study:

  • To develop and evaluate a novel statistical test for detecting zero inflation in binomial regression.
  • To provide a method for guiding the selection between standard binomial and zero-inflated binomial regression models.

Main Methods:

  • Development of a new test statistic based on comparing observed zeros to expected zeros under a binomial model.
  • Derivation of the closed-form test statistic and its asymptotic properties using estimating equations.
  • Comparison of the new test's performance against classical Wald, likelihood ratio, and score tests via simulation studies.

Main Results:

  • The newly developed test demonstrates strong performance across various scenarios.
  • The proposed test exhibits superior control of Type I errors compared to traditional methods, particularly when zero inflation is present.
  • The test provides a reliable approach for model selection in the presence of excess zeros.

Conclusions:

  • The new test offers a robust and accurate method for identifying zero inflation in binomial regression.
  • This test aids researchers in selecting the most appropriate statistical model, preventing biased inference and model complexity issues.
  • The method is validated through simulations and real-world data examples.