Scholarlink

<p>The Poisson regression model (PRM), which is commonly used to model count data, cannot be applied to over- or under-dispersed data sets. The Negative Binomial regression model (NBRM) is preferred as an alternative to the Poisson regression model for modeling overdispersed count data. As the degree of overdispersion increases, the performance of NBRM may decrease. In this situation, the Poisson Inverse Gaussian regression model (PIGRM) can serve as an alternative to PRM and NBRM for modeling highly overdispersed count data. We introduce and evaluate Liu-type estimators as a novel and efficient method to mitigate multicollinearity effects in the PIGRM, thereby improving the stability and accuracy of parameter estimates. A simulation study is carried out to examine the performance of the proposed estimators. Also, a real data example is provided to support the simulation results.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Poisson inverse Gaussian Liu-type estimator: a comparative study with Monte Carlo simulation

  • 期刊论文

  • 发表日期：

    2026年3月16日

查看全文
  • Melike Işılar,
  • Y. Murat Bulut

摘要

The Poisson regression model (PRM), which is commonly used to model count data, cannot be applied to over- or under-dispersed data sets. The Negative Binomial regression model (NBRM) is preferred as an alternative to the Poisson regression model for modeling overdispersed count data. As the degree of overdispersion increases, the performance of NBRM may decrease. In this situation, the Poisson Inverse Gaussian regression model (PIGRM) can serve as an alternative to PRM and NBRM for modeling highly overdispersed count data. We introduce and evaluate Liu-type estimators as a novel and efficient method to mitigate multicollinearity effects in the PIGRM, thereby improving the stability and accuracy of parameter estimates. A simulation study is carried out to examine the performance of the proposed estimators. Also, a real data example is provided to support the simulation results.