195 citations · 342 across the 12 of their papers we have counts for
12 papers
Nearly Optimal Subdata Selection
Min Yang, Wei Zheng, John Stufken +3
When, in terms of the number of data points, the size of a dataset exceeds available computing resources, or when labeling is expensive, an attractive solution consists of selectin…
Orthogonal Arrays: A Review
C. Devon Lin, John Stufken
Orthogonal arrays are arguably one of the most fascinating and important statistical tools for efficient data collection. They have a simple, natural definition, desirable properti…
Information-based Optimal Subdata Selection for Clusterwise Linear Regression
Yanxi Liu, John Stufken, Min Yang
Mixture-of-Experts models are commonly used when there exist distinct clusters with different relationships between the independent and dependent variables. Fitting such models for…
Factor selection in screening experiments by aggregation over random models
Rakhi Singh, John Stufken
Screening experiments are useful for screening out a small number of truly important factors from a large number of potentially important factors. The Gauss-Dantzig Selector (GDS)…
Information-Based Optimal Subdata Selection for Big Data Linear Regression
HaiYing Wang, Min Yang, John Stufken
Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large data sets due to computat…
Saturated locally optimal designs under differentiable optimality criteria
Linwei Hu, Min Yang, John Stufken
We develop general theory for finding locally optimal designs in a class of single-covariate models under any differentiable optimality criterion. Yang and Stufken [Ann. Statist. 4…