IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v28y2023i2d10.1007_s13253-023-00541-6.html
   My bibliography  Save this article

Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton

Author

Listed:
  • Sudipto Banerjee

    (UCLA Department of Biostatistics)

Abstract

No abstract is available for this item.

Suggested Citation

  • Sudipto Banerjee, 2023. "Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 365-369, June.
  • Handle: RePEc:spr:jagbes:v:28:y:2023:i:2:d:10.1007_s13253-023-00541-6
    DOI: 10.1007/s13253-023-00541-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-023-00541-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13253-023-00541-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
    2. Gramacy, Robert B., 2016. "laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i01).
    3. Christopher Wikle & Mevin Hooten, 2010. "Rejoinder on: A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 466-468, November.
    4. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    5. Lu Zhang & Sudipto Banerjee, 2022. "Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data," Biometrics, The International Biometric Society, vol. 78(2), pages 560-573, June.
    6. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    7. Matthias Katzfuss & Jonathan R. Stroud & Christopher K. Wikle, 2020. "Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 866-885, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    2. Giri Gopalan & Christopher K. Wikle, 2022. "A Higher-Order Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 22-45, March.
    3. Daniel W. Gladish & Daniel E. Pagendam & Luk J. M. Peeters & Petra M. Kuhnert & Jai Vaze, 2018. "Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 39-62, March.
    4. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    5. Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
    6. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    7. Hwang, Youngdeok & Kim, Hang J. & Chang, Won & Yeo, Kyongmin & Kim, Yongku, 2019. "Bayesian pollution source identification via an inverse physics model," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 76-92.
    8. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    9. Samantha M. Roth & Ben Seiyon Lee & Sanjib Sharma & Iman Hosseini‐Shakib & Klaus Keller & Murali Haran, 2023. "Flood hazard model calibration using multiresolution model output," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    10. Perrin, G., 2020. "Adaptive calibration of a computer code with time-series output," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    11. White, Staci A. & Herbei, Radu, 2015. "A Monte Carlo approach to quantifying model error in Bayesian parameter estimation," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 168-181.
    12. Mevin Hooten & Christopher Wikle & Michael Schwob, 2020. "Statistical Implementations of Agent‐Based Demographic Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 441-461, August.
    13. Matthew Bonas & Christopher K. Wikle & Stefano Castruccio, 2024. "Calibrated forecasts of quasi‐periodic climate processes with deep echo state networks and penalized quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    14. Leatherman, Erin R. & Dean, Angela M. & Santner, Thomas J., 2017. "Designing combined physical and computer experiments to maximize prediction accuracy," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 346-362.
    15. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
    16. Nott, David J. & Marshall, Lucy & Fielding, Mark & Liong, Shie-Yui, 2014. "Mixtures of experts for understanding model discrepancy in dynamic computer models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 491-505.
    17. Paulo, Rui & García-Donato, Gonzalo & Palomo, Jesús, 2012. "Calibration of computer models with multivariate output," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3959-3974.
    18. Jackson Samuel E. & Vernon Ian & Liu Junli & Lindsey Keith, 2020. "Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-33, April.
    19. Huang Huang & Stefano Castruccio & Marc G. Genton, 2022. "Forecasting high‐frequency spatio‐temporal wind power with dimensionally reduced echo state networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 449-466, March.
    20. Sondre Hølleland & Hans Arnfinn Karlsen, 2020. "A Stationary Spatio‐Temporal GARCH Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 177-209, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:28:y:2023:i:2:d:10.1007_s13253-023-00541-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.