21 citations · 47 across the 21 of their papers we have counts for
23 papers
Selective Inference in Graphical Models via Maximum Likelihood
Sofia Guglielmini, Gerda Claeskens, Snigdha Panigrahi
The graphical lasso is a widely used algorithm for fitting undirected Gaussian graphical models. However, for inference on functionals of edge values in the learned graph, standard…
Inference with Randomized Regression Trees
Soham Bakshi, Yiling Huang, Snigdha Panigrahi +1
Regression trees are a popular machine learning algorithm that fit piecewise constant models by recursively partitioning the predictor space. This paper focuses on statistical infe…
Selective Inference for Time-Varying Moderated Effects
Soham Bakshi, Walter Dempsey, Snigdha Panigrahi
Causal effect moderation investigates how the effect of interventions (or treatments) on outcome variables changes based on observed characteristics of individuals, known as potent…
Asymptotically-exact selective inference for quantile regression
Yumeng Wang, Snigdha Panigrahi, Xuming He
In modern data analysis, it is common to select a model before performing statistical inference. Selective inference tools make adjustments for the model selection process in order…
Selective Inference for Sparse Graphs via Neighborhood Selection
Yiling Huang, Snigdha Panigrahi, Walter Dempsey
Neighborhood selection is a widely used method used for estimating the support set of sparse precision matrices, which helps determine the conditional dependence structure in undir…
Selective inference using randomized group lasso estimators for general models
Yiling Huang, Sarah Pirenne, Snigdha Panigrahi +1
Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential famil…
Selective Inference with Distributed Data
Sifan Liu, Snigdha Panigrahi
As datasets grow larger, they are often distributed across multiple machines that compute in parallel and communicate with a central machine through short messages. In this paper,…
Exact Selective Inference with Randomization
Snigdha Panigrahi, Kevin Fry, Jonathan Taylor
We introduce a pivot for exact selective inference with randomization. Not only does our pivot lead to exact inference in Gaussian regression models, but it is also available in cl…
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging
Snigdha Panigrahi, Natasha Stewart, Chandra Sekhar Sripada +1
Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative t…
Treatment Effect Estimation with Efficient Data Aggregation
Snigdha Panigrahi, Jingshen Wang, Xuming He
Data aggregation, also known as meta analysis, is widely used to combine knowledge on parameters shared in common (e.g., average treatment effect) between multiple studies. In this…
Approximate Post-Selective Inference for Regression with the Group LASSO
Snigdha Panigrahi, Peter W. MacDonald, Daniel Kessler
After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected parameters is unreliable in…
Integrative Bayesian models using Post-selective Inference: a case study in Radiogenomics
Snigdha Panigrahi, Shariq Mohammed, Arvind Rao +1
Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clini…
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Basil Saeed, Snigdha Panigrahi, Caroline Uhler
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such…
Approximate selective inference via maximum likelihood
Snigdha Panigrahi, Jonathan Taylor
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…
Carving model-free inference
Snigdha Panigrahi
Complex studies involve many steps. Selecting promising findings based on pilot data is a first step. As more observations are collected, the investigator must decide how to combin…
Selection-adjusted inference: an application to confidence intervals for cis-eQTL effect sizes
Snigdha Panigrahi, Junjie Zhu, Chiara Sabatti
The goal of eQTL studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made such studies poss…
Maximal Moments and Uniform Modulus of Continuity for Stable Random Fields
Snigdha Panigrahi, Parthanil Roy, Yimin Xiao
In this work, we solve an open problem mentioned in Xiao (2010) and provide sharp bounds on the rate of growth of maximal moments for stationary symmetric stable random fields usin…
Kinematic Formula for Heterogeneous Gaussian Related Fields
Snigdha Panigrahi, Jonathan Taylor, Sreekar Vadlamani
We provide a generalization of the Gaussian Kinematic Formula (GKF) in Taylor(2006) for multivariate, heterogeneous Gaussian-related fields. The fields under consideration are non-…
A relevance-scalability-interpretability tradeoff with temporally evolving user personas
Snigdha Panigrahi, Nadia Fawaz
The current work characterizes the users of a VoD streaming space through user-personas based on a tenure timeline and temporal behavioral features in the absence of explicit user…
Scalable methods for Bayesian selective inference
Snigdha Panigrahi, Jonathan Taylor
Modeled along the truncated approach in Panigrahi (2016), selection-adjusted inference in a Bayesian regime is based on a selective posterior. Such a posterior is determined togeth…
An MCMC-free approach to post-selective inference
Snigdha Panigrahi, Jelena Markovic, Jonathan Taylor
We develop a Monte Carlo-free approach to inference post output from randomized algorithms with a convex loss and a convex penalty. The pivotal statistic based on a truncated law,…
Selective sampling after solving a convex problem
Xiaoying Tian Harris, Snigdha Panigrahi, Jelena Markovic +2
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 m…
Integrative Methods for Post-Selection Inference Under Convex Constraints
Snigdha Panigrahi, Jonathan Taylor, Asaf Weinstein
Inference after model selection has been an active research topic in the past few years, with numerous works offering different approaches to addressing the perils of the reuse of…