A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series. Issue 530 (2nd April 2020)

• Record Type: Journal Article
• Authors: Sung, Chih-Li; Hung, Ying; Rittase, William; Zhu, Cheng; Jeff Wu, C. F.
• Is Part Of: Journal of the American Statistical Association. Volume 115:Issue 530(2020)
• Page Start: 945
• Page End: 956
• Publication Date: 2020-04-02
• Subjects: Computer experiment -- Gaussian process model -- Single molecule experiment -- Uncertainty quantification; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2019.1604361
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: 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. Supplementary materials for this article are available online.