activity
20052023
most citedResampling methods for spatial regression models under a class of stochastic designs

68 citations · 446 across the 32 of their papers we have counts for

collaborators

34 papers

stat.ML2023

Online Bootstrap Inference with Nonconvex Stochastic Gradient Descent Estimator

Yanjie Zhong, Todd Kuffner, Soumendra Lahiri

In this paper, we investigate the theoretical properties of stochastic gradient descent (SGD) for statistical inference in the context of nonconvex optimization problems, which hav…

stat.ML2023

Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks

Sweta Rai, Alexis Hoffman, Soumendra Lahiri +3

The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. H…

math.ST2022

On optimal block resampling for Gaussian-subordinated long-range dependent processes

Qihao Zhang, Soumendra N. Lahiri, Daniel J. Nordman

Block-based resampling estimators have been intensively investigated for weakly dependent time processes, which has helped to inform implementation (e.g., best block sizes). Howeve…

math.HO2021

Harmonic numbers as the summation of integrals

N. Karjanto

Harmonic numbers arise from the truncation of the harmonic series. The harmonic number is the sum of the reciprocals of each positive integer up to . In addition t…

math.PR2020

Central Limit Theorem in High Dimensions : The Optimal Bound on Dimension Growth Rate

Debraj Das, Soumendra Lahiri

In this article, we try to give an answer to the simple question: ``\textit{What is the critical growth rate of the dimension as a function of the sample size for which the…

stat.ME2020

Two-sample Testing on Latent Distance Graphs With Unknown Link Functions

Yiran Wang, Minh Tang, Soumendra Nath Lahiri

We propose a valid and consistent test for the hypothesis that two latent distance random graphs on the same vertex set have the same generating latent positions, up to some uniden…