72 citations · 231 across the 61 of their papers we have counts for
13 papers · 1 filter
Rectangular Hull Confidence Regions for Multivariate Parameters
Aniket Jain, Arun K Kuchibhotla
We introduce three notions of multivariate median bias, namely, rectilinear, Tukey, and orthant median bias. Each of these median biases is zero under a suitable notion of multivar…
Estimating the Likelihood of Arrest from Police Records in Presence of Unreported Crimes
Riccardo Fogliato, Arun Kumar Kuchibhotla, Zachary Lipton +3
Many important policy decisions concerning policing hinge on our understanding of how likely various criminal offenses are to result in arrests. Since many crimes are never reporte…
Generalized Asymptotic Limit Theory and Inference for Isotonic Regression
Soham Mallick, Siddhaarth Sarkar, Arun Kumar Kuchibhotla
Monotonicity is a natural shape constraint in nonparametric regression problems, arising for instance when predicting factory yield as a monotone function of labor hours. The widel…
Forster-Warmuth Counterfactual Regression: A Unified Learning Approach
Yachong Yang, Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen
Series or orthogonal basis regression is one of the most popular non-parametric regression techniques in practice, obtained by regressing the response on features generated by eval…
From Isotonic to Lipschitz Regression: A New Interpolative Perspective on Shape-restricted Estimation
Kenta Takatsu, Tianyu Zhang, Arun Kumar Kuchibhotla
This manuscript bridges nonparametric smoothness-based and shape-restricted estimation, which may appear as two disjoint paradigms in the field. The proposed approach is motivated…
Inference for Projection Parameters in Linear Regression: beyond
Woonyoung Chang, Arun Kumar Kuchibhotla, Alessandro Rinaldo
We consider the problem of inference for projection parameters in linear regression with increasing dimensions. This problem has been studied under a variety of assumptions in the…
Central Limit Theorems and Approximation Theory: Part II
Arun Kumar Kuchibhotla
In Part I of this article (Banerjee and Kuchibhotla (2023)), we have introduced a new method to bound the difference in expectations of an average of independent random vector and…
Central Limit Theorems and Approximation Theory: Part I
Arisina Banerjee, Arun K Kuchibhotla
Central limit theorems (CLTs) have a long history in probability and statistics. They play a fundamental role in constructing valid statistical inference procedures. Over the last…
Dual Induction CLT for High-dimensional m-dependent Data
Heejong Bong, Arun Kumar Kuchibhotla, Alessandro Rinaldo
We derive novel and sharp high-dimensional Berry--Esseen bounds for the sum of -dependent random vectors over the class of hyper-rectangles exhibiting only a poly-logarithmic de…
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Jin-Hong Du, Pratik Patil, Arun Kumar Kuchibhotla
We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges…
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision
Siddhaarth Sarkar, Arun Kumar Kuchibhotla
Conformal inference has played a pivotal role in providing uncertainty quantification for black-box ML prediction algorithms with finite sample guarantees. Traditionally, conformal…
Tight Concentration Inequality for Sub-Weibull Random Variables with Generalized Bernstien Orlicz norm
Heejong Bong, Arun Kumar Kuchibhotla
Recent development in high-dimensional statistical inference has necessitated concentration inequalities for a broader range of random variables. We focus on sub-Weibull random var…
Extrapolated cross-validation for randomized ensembles
Jin-Hong Du, Pratik Patil, Kathryn Roeder +1
Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of en…