Simultaneous inference for model averaging of derived parameters

TY - JOUR

T1 - Simultaneous inference for model averaging of derived parameters

AU - Jensen, Signe Marie

AU - Ritz, Christian

N1 - CURIS 2015 NEXS 071

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

U2 - 10.1111/risa.12242

DO - 10.1111/risa.12242

M3 - Journal article

C2 - 24952957

SN - 0272-4332

VL - 35

SP - 68

EP - 76

JO - Risk Analysis

JF - Risk Analysis

IS - 1

ER -