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Simultaneous Inference for Model Averaging of Derived Parameters

机译:衍生参数模型平均的同时推断

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摘要

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.
机译:模型平均是用于捕获由于模型选择而引起的不确定性的有用方法。当前,这种不确定性通常通过不容易扩展到同时推断的近似来量化。而且,在实践中,既需要模型平均,又需要同时推断在后拟合步骤中计算出的导出参数。我们提出了一种方法,用于获得一个或几个模型平均估计的派生参数的渐近正确标准误差,并获得渐近控制家庭式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,并通过三个具体示例证明了该方法的适用性。

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  • 来源
    《Risk analysis》 |2015年第1期|68-76|共9页
  • 作者单位

    Department of Nutrition, Exercise and Sports, University of Copenhagen, Nrregade 10, 1165 Kbenhavn, Denmark ,Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg, Denmark;

    Department of Nutrition, Exercise and Sports, University of Copenhagen, Nrregade 10, 1165 Kbenhavn, Denmark;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Asymptotic representation; benchmark dose; coverage; dose response; Wald-type intervals;

    机译:渐近表示;基准剂量;覆盖范围剂量反应沃尔德型间隔;

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