52 citations · 74 across the 4 of their papers we have counts for
4 papers
A reinforced learning approach to optimal design under model uncertainty
Mingyao Ai, Holger Dette, Zhengfu Liu +1
Optimal designs are usually model-dependent and likely to be sub-optimal if the postulated model is not correctly specified. In practice, it is common that a researcher has a list…
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
Jun Yu, HaiYing Wang, Mingyao Ai +1
Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with r…
Optimal Subsampling Algorithms for Big Data Regressions
Mingyao Ai, Jun Yu, Huiming Zhang +1
To fast approximate maximum likelihood estimators with massive data, this paper studies the Optimal Subsampling Method under the A-optimality Criterion (OSMAC) for generalized line…
Optimal designs for the proportional interference model
Kang Li, Wei Zheng, Mingyao Ai
The interference model has been widely used and studied in block experiments where the treatment for a particular plot has effects on its neighbor plots. In this paper, we study op…