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Estimation for zero-inflated over-dispersed count data model with missing response.

Rajibul Mian1, Sudhir Paul1

  • 1Department of Mathematics and Statistics, University of Windsor, Windsor, ON N9B 3P4, Canada.

Statistics in Medicine
|September 2, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a method for estimating parameters in zero-inflated count models with missing data, specifically using a weighted expectation maximization algorithm for the zero-inflated extended negative binomial model.

Keywords:
EM algorithmcount dataover dispersionregression modelzero inflation

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Count Data Analysis

Background:

  • Missing data in statistical analyses can lead to biased parameter estimates.
  • Zero-inflated count models are frequently used in various fields but are sensitive to missing responses.
  • Accurate parameter estimation is crucial for reliable inference in these models.

Purpose of the Study:

  • To develop an estimation procedure for parameters of zero-inflated over-dispersed/under-dispersed count models when responses are missing.
  • To specifically address the zero-inflated extended negative binomial model in the context of missing data.
  • To evaluate the performance and robustness of the proposed estimation method.

Main Methods:

  • Development of a weighted expectation maximization (EM) algorithm for maximum likelihood estimation.
  • Application of the weighted EM algorithm to a zero-inflated extended negative binomial model with missing responses.
  • Conducting simulation studies to assess the properties of the developed estimators.

Main Results:

  • The weighted EM algorithm provides reliable parameter estimates for the zero-inflated extended negative binomial model with missing data.
  • Simulation results demonstrate good performance of the proposed estimation procedure.
  • The method shows robustness when applied to other over-dispersed count data models, including log-normal mixture of Poisson and zero-inflated Poisson models.

Conclusions:

  • The proposed weighted EM algorithm is an effective approach for parameter estimation in zero-inflated count models with missing responses.
  • The methodology is robust and applicable to a range of over-dispersed count data distributions.
  • The study provides a practical and reliable statistical procedure for handling missing data in complex count data scenarios.