195 citations · 288 across the 7 of their papers we have counts for
7 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…
On optimal designs for non-regular models
Yi Lin, Ryan Martin, Min Yang
Classically, Fisher information is the relevant object in defining optimal experimental designs. However, for models that lack certain regularity, the Fisher information does not e…
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 Proven statistical methods are no longer applicable with extraordinary large data sets due to computat…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…
On the de la Garza Phenomenon
Min Yang
Deriving optimal designs for nonlinear models is in general challenging. One crucial step is to determine the number of support points needed. Current tools handle this on a case-b…
Support points of locally optimal designs for nonlinear models with two parameters
Min Yang, John Stufken
We propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, w…