  R. A. Fisher introduced the concept of fiducial as a potential replacement
for the Bayesian posterior distribution in the 1930s. During the past century,
fiducial approaches have been explored in various parametric and nonparametric
settings. However, to the best of our knowledge, no fiducial inference has been
developed in the realm of semiparametric statistics. In this paper, we propose
a novel fiducial approach for semiparametric models. To streamline our
presentation, we use the Cox proportional hazards model, which is the most
popular model for the analysis of survival data, as a running example. Other
models and extensions are also discussed. In our experiments, we find our
method to perform well especially in situations when the maximum likelihood
estimator fails.
