Sensitivity analysis of wind energy resources with Bayesian non-Gaussian and nonstationary functional ANOVA-Reference-Cited by-同舟云学术

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Sensitivity analysis of wind energy resources with Bayesian non-Gaussian and nonstationary functional ANOVA

Published:2024-03-01 Issue:1 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Zhang Jiachen¹,Crippa Paola²,Genton Marc G.³,Castruccio Stefano¹

Affiliation:

1. Department of Applied and Computational Mathematics and Statistics, University of Notre Dame

2. Department of Civil and Environmental Engineering & Geosciences, University of Notre Dame

3. Statistics Program, King Abdullah University of Science and Technology

Publisher

Institute of Mathematical Statistics

Reference54 articles.

1. FUGLSTAD, G.-A., SIMPSON, D., LINDGREN, F. and RUE, H. (2019). Constructing priors that penalize the complexity of Gaussian random fields. J. Amer. Statist. Assoc. 114 445–452.

2. BOLIN, D. and LINDGREN, F. (2011). Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping. Ann. Appl. Stat. 5 523–550.

3. WHITTLE, P. (1954). On stationary processes in the plane. Biometrika 41 434–449.

4. Chen, W., Castruccio, S., Genton, M. G. and Crippa, P. (2018). Current and future estimates of wind energy potential over Saudi Arabia. J. Geophys. Res., Atmos. 123 6443–6459.

5. Gualtieri, G. (2019). A comprehensive review on wind resource extrapolation models applied in wind energy. Renew. Sustain. Energy Rev. 102 215–233.

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