72 citations · 231 across the 61 of their papers we have counts for
7 papers · 1 filter
Model-free Study of Ordinary Least Squares Linear Regression
Arun K. Kuchibhotla, Lawrence D. Brown, Andreas Buja
Ordinary least squares (OLS) linear regression is one of the most basic statistical techniques for data analysis. In the main stream literature and the statistical education, the s…
Deterministic Inequalities for Smooth M-estimators
Arun Kumar Kuchibhotla
Ever since the proof of asymptotic normality of maximum likelihood estimator by Cramer (1946), it has been understood that a basic technique of the Taylor series expansion suffices…
Assumption Lean Regression
Richard A. Berk, Andreas Buja, Lawrence Brown +4
It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim t…
Valid Post-selection Inference in Assumption-lean Linear Regression
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja +2
Construction of valid statistical inference for estimators based on data-driven selection has received a lot of attention in the recent times. Berk et al. (2013) is possibly the fi…
High-dimensional CLT: Improvements, Non-uniform Extensions and Large Deviations
Arun Kumar Kuchibhotla, Somabha Mukherjee, Debapratim Banerjee
Central limit theorems (CLTs) for high-dimensional random vectors with dimension possibly growing with the sample size have received a lot of attention in the recent times. Chernoz…
Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression
Arun Kumar Kuchibhotla, Abhishek Chakrabortty
Concentration inequalities form an essential toolkit in the study of high dimensional (HD) statistical methods. Most of the relevant statistics literature in this regard is based o…
Uniform-in-Submodel Bounds for Linear Regression in a Model Free Framework
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja +2
For the last two decades, high-dimensional data and methods have proliferated throughout the literature. Yet, the classical technique of linear regression has not lost its usefulne…