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Estimation for zero-inflated beta-binomial regression model with missing response data.

Rong Luo1, Sudhir Paul1

  • 1Department of Mathematics and Statistics, University of Windsor, Windsor, ON, Canada.

Statistics in Medicine
|June 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing toxicological data with missing values. The weighted expectation maximization algorithm effectively estimates parameters in complex binomial models, showing superior performance in simulations.

Keywords:
EM algorithmbinomial dataoverdispersionregression modelzero inflation

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Area of Science:

  • Biostatistics
  • Toxicology
  • Statistical Modeling

Background:

  • Discrete proportional data with overdispersion and zero inflation are common in toxicology.
  • Missing responses in regression analysis pose significant challenges for accurate parameter estimation.

Purpose of the Study:

  • To develop an estimation procedure for zero-inflated, overdispersed binomial models with missing responses.
  • To evaluate the performance of the proposed method under different missing data mechanisms.

Main Methods:

  • Utilized a weighted expectation maximization (WEM) algorithm for maximum likelihood estimation.
  • Conducted extensive simulations to assess estimation properties (bias, variance, MSE, coverage).

Main Results:

  • The WEM algorithm demonstrated superior performance in estimating parameters for the complex binomial model.
  • Simulation results indicated improved accuracy and reliability of estimates compared to standard methods.

Conclusions:

  • The proposed WEM-based estimation procedure is effective for handling missing data in zero-inflated, overdispersed binomial models.
  • This approach offers a robust solution for toxicological and similar data analysis challenges.