Abstract

In this paper the influence measures for the Negative Binomial regression model are presented. Based on the conditional expectation of the complete-data log-likelihood function we derive some influence measures, such as case deletion (global influence) and local influence analysis. For the implementation of the influence measures we present explicit expressions and discuss an appropriate perturbation scheme. To illustrate the results, simulations and real data applications are presented. Results show that both global and local influence methods are effective in detecting possible observations that influence the parameter estimation, or at least in focusing researchers attention on those observations.

摘要

在本文中，介绍了负二项式回归模型的影响度量。基于完整数据对数似然函数的条件期望，我们推导出一些影响度量，例如案例删除（全局影响）和局部影响分析。为了实施影响措施，我们提出了明确的表达式并讨论了适当的扰动方案。为了说明结果，提供了模拟和实际数据应用。结果表明，全局和局部影响方法都可以有效地检测影响参数估计的可能观察结果，或者至少可以将研究人员的注意力集中在这些观察结果上。