在“bootComb”R包中放宽参数独立性假设
Relaxation of the parameter independence assumption in the `bootComb` R package
摘要：
背景bootComb R软件包允许研究人员针对任意数量的独立估计参数的任意组合导出具有正确目标覆盖率的置信区间。bootComb的早期版本（< 1.1.0）使用独立的bootstrap采样，并要求参数本身是独立的-在某些实际应用中，这是一个不切实际的假设。调查结果。使用Gaussian copula来定义参数之间的依赖关系，bootComb包已经扩展为允许依赖参数。影响。更新后的bootComb包现在可以处理依赖参数的情况，用户指定定义依赖结构的相关矩阵。虽然在实践中可能很难知道参数之间的确切相关性结构，但“bootComb”允许运行敏感性分析，以评估参数相关性对组合参数的最终置信区间的影响。空房的bootComb可从Comprehensive R Archive Network（https：//CRAN.R-project.org/package=bootComb）获得。
英文摘要：
Background. The bootComb R package allows researchers to derive confidence intervals with correct target coverage for arbitrary combinations of arbitrary numbers of independently estimated parameters. Previous versions (< 1.1.0) of bootComb used independent bootstrap sampling and required that the parameters themselves are independent - an unrealistic assumption in some real-world applications. Findings. Using Gaussian copulas to define the dependence between parameters, the bootComb package has been extended to allow for dependent parameters. Implications. The updated bootComb package can now handle cases of dependent parameters, with users specifying a correlation matrix defining the dependence structure. While in practice it may be difficult to know the exact dependence structure between parameters, `bootComb` allows running sensitivity analyses to assess the impact of parameter dependence on the resulting confidence interval for the combined parameter. Availability. bootComb is available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=bootComb).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
