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

Chih Li Sung, Ying Hung, William Rittase, Cheng Zhu, C. F. Jeff Wu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)945-956
Number of pages12
JournalJournal of the American Statistical Association
Issue number530
StatePublished - Apr 2 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Computer experiment
  • Gaussian process model
  • Single molecule experiment
  • Uncertainty quantification


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