Snigdha Panigrahi

Snigdha Panigrahi
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Snigdha Panigrahi

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

Publications Authored By Snigdha Panigrahi

Characterization of consumers has been explored previously in prior works with different goals. The current work presents user personas in a VoD streaming space using a tenure timeline and temporal behavioral features in the absence of explicit user profiles. A choice of tenure timeline caters to business needs of understanding the evolution and phases of user behavior as their accounts age while temporal characteristics are necessary to capture the dynamic aspect of user personas labels. Read More

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 \citet{yekutieli2012adjusted}'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