4 citations · 5 across the 6 of their papers we have counts for
13 papers
Mitigating multiple descents: A model-agnostic framework for risk monotonization
Pratik Patil, Arun Kumar Kuchibhotla, Yuting Wei +1
Recent empirical and theoretical analyses of several commonly used prediction procedures reveal a peculiar risk behavior in high dimensions, referred to as double/multiple descent,…
maars: Tidy Inference under the 'Models as Approximations' Framework in R
Riccardo Fogliato, Shamindra Shrotriya, Arun Kumar Kuchibhotla
Linear regression using ordinary least squares (OLS) is a critical part of every statistician's toolkit. In R, this is elegantly implemented via lm() and its related functions. How…
Median bias of M-estimators
Arun Kumar Kuchibhotla
In this note, we derive bounds on the median bias of univariate M-estimators under mild regularity conditions. These requirements are not sufficient to imply convergence in distrib…
Nested Conformal Prediction Sets for Classification with Applications to Probation Data
Arun K. Kuchibhotla, Richard A. Berk
Risk assessments to help inform criminal justice decisions have been used in the United States since the 1920s. Over the past several years, statistical learning risk algorithms ha…
High-dimensional CLT for Sums of Non-degenerate Random Vectors: -rate
Arun Kumar Kuchibhotla, Alessandro Rinaldo
In this note, we provide a Berry--Esseen bounds for rectangles in high-dimensions when the random vectors have non-singular covariance matrices. Under this assumption of non-singul…
All of Linear Regression
Arun K. Kuchibhotla, Lawrence D. Brown, Andreas Buja +1
Least squares linear regression is one of the oldest and widely used data analysis tools. Although the theoretical analysis of the ordinary least squares (OLS) estimator is as old,…