Simultaneous Inference for Model Averaging of Derived Parameters

Authors

  • Signe M. Jensen,

    Corresponding author
    1. Department of Nutrition, Exercise and Sports, University of Copenhagen, Kbenhavn, Denmark
    • Address correspondence to Signe M. Jensen, Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg, Denmark; jensen.signe@gmail.com.

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  • Christian Ritz

    1. Department of Nutrition, Exercise and Sports, University of Copenhagen, Kbenhavn, Denmark
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Errata

This article is corrected by:

  1. Errata: Erratum to “Simultaneous Inference for Model Averaging of Derived Parameters” by Signe M. Jensen and Christian Ritz, in Risk Analysis, 35(1):68–76 Volume 35, Issue 2, 344, Article first published online: February 2015

Abstract

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.

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