Efficient and effective calibration of numerical model outputs using hierarchical dynamic models-Reference-Cited by-同舟云学术

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Efficient and effective calibration of numerical model outputs using hierarchical dynamic models

Published:2024-06-01 Issue:2 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Chen Yewen¹,Chang Xiaohui²,Zhang Bohai³,Huang Hui⁴

Affiliation:

1. College of Public Health, University of Georgia

2. College of Business, Oregon State University

3. Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College

4. Center for Applied Statistics and School of Statistics, Renmin University of China

Publisher

Institute of Mathematical Statistics

Reference83 articles.

1. Kim, H.-M., Mallick, B. K. and Holmes, C. C. (2005). Analyzing nonstationary spatial data using piecewise Gaussian processes. J. Amer. Statist. Assoc. 100 653–668.

2. Salter, J. M., Williamson, D. B., Scinocca, J. and Kharin, V. (2019). Uncertainty quantification for computer models with spatial output using calibration-optimal bases. J. Amer. Statist. Assoc. 114 1800–1814.

3. KAUFMAN, C. G., SCHERVISH, M. J. and NYCHKA, D. W. (2008). Covariance tapering for likelihood-based estimation in large spatial data sets. J. Amer. Statist. Assoc. 103 1545–1555.

4. HEATON, M. J., CHRISTENSEN, W. F. and TERRES, M. A. (2017). Nonstationary Gaussian process models using spatial hierarchical clustering from finite differences. Technometrics 59 93–101.

5. NYCHKA, D., BANDYOPADHYAY, S., HAMMERLING, D., LINDGREN, F. and SAIN, S. (2015). A multiresolution Gaussian process model for the analysis of large spatial datasets. J. Comput. Graph. Statist. 24 579–599.

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