A generalized Gaussian process model for computer experiments with binary time series

DOI10.48550/arXiv.1705.02511zbMath1445.62242arXiv1705.02511OpenAlexW2964279610MaRDI QIDQ153075

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

Publication date: 6 May 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.02511

zbMATH Keywords

uncertainty quantificationcomputer experimentGaussian process modelsingle molecule experiment

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Gaussian processes (60G15) Design of statistical experiments (62K99)

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Uses Software

• R
• Kernlab
• GPFDA
• logitnorm

Cites Work

• Efficient calibration for imperfect computer models
• Calibrating a large computer experiment simulating radiative shock hydrodynamics
• The importance of scale for spatial-confounding bias and precision of spatial regression estimators
• Bayesian emulation of complex multi-output and dynamic computer models
• Multivariate adaptive regression splines
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Asymptotics for REML estimation of spatial covariance parameters
• The asymptotic distribution of REML estimators
• Nonstationary multivariate process modeling through spatially varying coregionalization
• Bayesian Calibration of Computer Models
• Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
• Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
• Binary Time Series Modeling With Application to Adhesion Frequency Experiments
• On Estimation and Prediction for Spatial Generalized Linear Mixed Models
• Markov Regression Models for Time Series: A Quasi-Likelihood Approach
• Two Moments of the Logitnormal Distribution
• Logistic-Normal Distributions: Some Properties and Uses
• Partial likelihood
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• Orthogonal Array-Based Latin Hypercubes
• Generalized Autoregressive Moving Average Models
• Exploiting Variance Reduction Potential in Local Gaussian Process Search
• Orthogonal Gaussian process models
• Approximate Inference in Generalized Linear Mixed Models
• Bayesian inference for variance components using only error contrasts
• Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Recovery of inter-block information when block sizes are unequal
• A dynamic modelling strategy for Bayesian computer model emulation
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