activity
20242026
collaborators

21 papers

math.PR2026

On the L{é}vy concentration function of Gaussian quadratic forms with applications to second order U-statistics

Abhimanyu Choudhary, Arun Kumar Kuchibhotla

We provide an upper-bound for the L{é}vy concentration function: where is a weighted sum…

stat.ME2026

Doubly Robust and Efficient Calibration of Prediction Sets for Right-Censored Time-to-Event Outcomes

Rebecca Farina, Eric J. Tchetgen Tchetgen, Arun Kumar Kuchibhotla

Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right-censoring with guaranteed coverage. Inspired by modern conformal inferenc…

math.ST2026

Honest Inference for Stochastic Optimization

Kenta Takatsu, Arun Kumar Kuchibhotla

This manuscript studies a general approach to construct confidence sets for the solution of stochastic optimization, rendering empirical risk minimization as special cases. Statist…

stat.ME2026

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…

math.PR2026

Berry-Esseen bounds for multivariate martingale difference sequences in the Kolmogorov distance

Weichen Wu, Dung Le, Arun Kumar Kuchibhotla +1

We derive new Gaussian approximation for finite martingale difference sequences in with respect to the Kolmogorov distance. Under appropriate conditions, our bounds…

math.ST2026

On a Probability Inequality for Order Statistics with Applications to Bootstrap, Conformal Prediction, and more

Manit Paul, Arun Kumar Kuchibhotla

``Behind every limit theorem, there is an inequality'' said Kolmogorov. We say ``for every inequality, there is an approximate inequality under approximate regularity conditions.''…