99 citations · 128 across the 12 of their papers we have counts for
5 papers · 1 filter
Statistical Inference on Grayscale Images via the Euler-Radon Transform
Kun Meng, Mattie Ji, Jinyu Wang +4
Tools from topological data analysis have been widely used to represent binary images in many scientific applications. Methods that aim to represent grayscale images (i.e., where p…
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Hammaad Adam, Fan Yin, Huibin +5
Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment ear…
A Simple Approach for Local and Global Variable Importance in Nonlinear Regression Models
Emily T. Winn-Nuñez, Maryclare Griffin, Lorin Crawford
The ability to interpret machine learning models has become increasingly important as their usage in data science continues to rise. Most current interpretability methods are optim…
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study
Lorin Crawford, Seth R. Flaxman, Daniel E. Runcie +1
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions…
Bayesian Approximate Kernel Regression with Variable Selection
Lorin Crawford, Kris C. Wood, Xiang Zhou +1
Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression mod…