Jensen Signe M, Ritz Christian
Department of Nutrition, Exercise and Sports, University of Copenhagen, Nrregade 10, 1165, Kbenhavn, Denmark.
Risk Anal. 2015 Jan;35(1):68-76. doi: 10.1111/risa.12242. Epub 2014 Jun 20.
Model averaging is a useful approach for capturing uncertainty due to model selection. Currently, this uncertainty is often quantified by means of approximations that do not easily extend to simultaneous inference. Moreover, in practice there is a need for both model averaging and simultaneous inference for derived parameters calculated in an after-fitting step. We propose a method for obtaining asymptotically correct standard errors for one or several model-averaged estimates of derived parameters and for obtaining simultaneous confidence intervals that asymptotically control the family-wise Type I error rate. The performance of the method in terms of coverage is evaluated using a simulation study and the applicability of the method is demonstrated by means of three concrete examples.
模型平均是一种用于捕捉因模型选择而产生的不确定性的有用方法。目前，这种不确定性通常通过不易扩展到同时推断的近似方法来量化。此外，在实践中，对于在拟合后步骤中计算的派生参数，既需要模型平均又需要同时推断。我们提出了一种方法，用于获得派生参数的一个或多个模型平均估计的渐近正确标准误差，并用于获得渐近控制族wise I型错误率的同时置信区间。通过模拟研究评估了该方法在覆盖率方面的性能，并通过三个具体例子展示了该方法的适用性。
