• Publications
  • Influence
A Model Free Perspective for Linear Regression: Uniform-in-model Bounds for Post Selection Inference
For the last two decades, high-dimensional data and methods have proliferated throughout the literature. The classical technique of linear regression, however, has not lost its touch in applications.Expand
  • 17
  • 6
  • PDF
Efficient Estimation in Convex Single Index Models
Abstract: We consider estimation and inference in a single index regression model with an unknown convex link function. We propose two estimators for the unknown link function: (1) a LipschitzExpand
  • 12
  • 3
  • PDF
Valid Post-selection Inference in Assumption-lean Linear Regression
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 first work to provideExpand
  • 7
  • 2
  • PDF
Efficient Estimation in Single Index Models through Smoothing splines.
We consider estimation and inference in a single index regression model with an unknown but smooth link function. In contrast to the standard approach of using kernels or regression splines, we useExpand
  • 12
  • 1
  • PDF
Nested conformal prediction and quantile out-of-bag ensemble methods
TLDR
Conformal prediction is a popular tool for distribution-free uncertainty quantification in statistical learning, we provide an alternate (but equivalent) view that starts with a sequence of nested sets and calibrates them to find a valid prediction region. Expand
  • 7
  • 1
  • PDF
Models as Approximations—Rejoinder
TLDR
We respond to the discussants of our articles emphasizing the importance of inference under misspecification in the context of the reproducibility/ replicability crisis. Expand
  • 1
  • 1
Assumption Lean Regression
Abstract It is well known that with observational data, models used in conventional regression analyses are commonly misspecified. Yet in practice, one tends to proceed with interpretations andExpand
  • 4
  • 1
  • PDF
Model-free Study of Ordinary Least Squares Linear Regression
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 study of linearExpand
  • 3
  • 1
  • PDF
Nested Conformal Prediction and the Generalized Jackknife
We provide an alternate unified framework for conformal prediction, which is a framework to provide assumption-free prediction intervals. Instead of beginning by choosing a conformity score, ourExpand
  • 5
Statistical inference based on bridge divergences
M-estimators offer simple robust alternatives to the maximum likelihood estimator. The density power divergence (DPD) and the logarithmic density power divergence (LDPD) measures provide two classesExpand
  • 2
  • PDF