Approximate selective inference via maximum likelihood

Understand

Several strategies have been developed recently to ensure valid inference after model selection; some of these are easy to compute, while others fare better in terms of inferential power.

• In this paper, we consider a selective inference framework for Gaussian data.
• We propose a new method for inference through approximate maximum likelihood estimation.
• Our goal is to: (i) achieve better inferential power with the aid of randomization, (ii) bypass expensive MCMC sampling from exact conditional distributions that are hard to evaluate in closed forms.