Forskning ved Københavns Universitet - Københavns Universitet


Simultaneous inference for model averaging of derived parameters

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

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.

TidsskriftRisk Analysis
Udgave nummer1
Sider (fra-til)68-76
Antal sider9
StatusUdgivet - 2015

Bibliografisk note

CURIS 2015 NEXS 071

ID: 125302518