A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

• 成果类型: Article
• 署名作者: Sung, Chih-Li; Hung, Ying; Rittase, William; Zhu, Cheng; Wu, C. F. Jeff
• 署名单位: Michigan State University; Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1604361
• 发表日期: 2020
• 页码: 945-956
• 关键词: regression-models; likelihood; calibration; prediction; inference; DESIGN

摘要

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments. for this article are available online.