Snigdha Panigrahi

Snigdha Panigrahi
Are you Snigdha Panigrahi?

Claim your profile, edit publications, add additional information:

Contact Details

Snigdha Panigrahi

Pubs By Year

Pub Categories

Statistics - Methodology (3)
Statistics - Theory (1)
Mathematics - Statistics (1)

Publications Authored By Snigdha Panigrahi

The current work proposes a Monte Carlo free alternative to inference post randomized selection algorithms with a convex loss and a convex penalty. The pivots based on the selective law that is truncated to all selected realizations, typically lack closed form expressions in randomized settings. Inference in these settings relies upon standard Monte Carlo sampling techniques, which can be prove to be unstable for parameters far off from the chosen reference distribution. Read More

Adopting the Bayesian methodology of adjusting for selection to provide valid inference in Panigrahi (2016), the current work proposes an approximation to a selective posterior, post randomized queries on data. Such a posterior differs from the usual one as it involves a truncated likelihood prepended with a prior belief on parameters in a Bayesian model. The truncation, imposed by selection, leads to intractability of the selective posterior, thereby posing a technical hurdle in sampling from such a posterior. Read More

We consider the problem of selective inference after solving a (randomized) convex statistical learning program in the form of a penalized or constrained loss function. Our first main result is a change-of-measure formula that describes many conditional sampling problems of interest in selective inference. Our approach is model-agnostic in the sense that users may provide their own statistical model for inference, we simply provide the modification of each distribution in the model after the selection. Read More

We provide Bayesian inference for a linear model selected after observing the data. Adopting Yekutieli (2012)'s ideas, the Bayesian model consists of a prior and a truncated likelihood. The resulting posterior distribution, unlike in the setup usually considered when performing Bayesian variable selection, is affected by the very fact that selection was applied. Read More