Simultaneous inference for model averaging of derived parameters.

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… (More)
DOI: 10.1111/risa.12242

Topics

  • Presentations referencing similar topics