Discussion Paper
Sensitivity Analysis in Semiparametric Likelihood Models
We provide methods for inference on a finite dimensional parameter of interest, θ in Re^{d_θ}, in a semiparametric probability model when an infinite dimensional nuisance parameter, g, is present. We depart from the semiparametric literature in that we do not require that the pair (θ,g) is point identified and so we construct confidence regions for θ that are robust to non-point identification.