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References (22)
-
M. Iturbide, J. Gutiérrez, L. Alves, J. Bedia, R. Cerezo-Mota, E. Cimadevilla, A. Cofiño, A. Luca, S. Faria, I. Gorodetskaya, M. Hauser, S. Herrera, K. Hennessy, H. Hewitt, Richard Jones, S. Krakovska, R. Manzanas, D. Martínez-Castro, G. Narisma, I. Nurhati, Izidine Pinto, S. Seneviratne, B. Hurk, C. Vera (2020)
An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasetsEarth System Science Data
-
M. Genton, S. Castruccio, P. Crippa, S. Dutta, Raphael Huser, Ying Sun, Sabrina Vettori (2015)
Visuanimation in statisticsStat, 4
-
S. Castruccio, M. Genton (2016)
Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal TemperatureTechnometrics, 58
-
S. López-Pintado, J. Romo (2009)
On the Concept of Depth for Functional DataJournal of the American Statistical Association, 104
-
J. Guinness, D. Hammerling (2016)
Compression and Conditional Emulation of Climate Model OutputJournal of the American Statistical Association, 113
-
Ying Sun, M. Genton (2011)
Functional BoxplotsJournal of Computational and Graphical Statistics, 20
-
J. Oakley, A. O’Hagan (2004)
Probabilistic sensitivity analysis of complex models: a Bayesian approachJournal of the Royal Statistical Society: Series B (Statistical Methodology), 66
-
Richard Jones (1963)
Stochastic Processes on a SphereAnnals of Mathematical Statistics, 34
-
S. Castruccio, J. Guinness (2015)
An evolutionary spectrum approach to incorporate large‐scale geographical descriptors on global processesJournal of the Royal Statistical Society: Series C (Applied Statistics), 66
-
Won Chang, M. Haran, P. Applegate, D. Pollard (2015)
Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial DataJournal of the American Statistical Association, 111
-
S. Castruccio, M. Genton (2018)
Principles for statistical inference on big spatio-temporal data from climate modelsStatistics & Probability Letters, 136
-
V. Eyring, S. Bony, G. Meehl, C. Senior, B. Stevens, R. Stouffer, K. Taylor (2015)
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organizationGeoscientific Model Development, 9
-
Simon Mak, Chih-Li Sung, Xingjian Wang, Shiang-Ting Yeh, Yu-Hung Chang, V. Joseph, V. Yang, C. Wu (2016)
An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy SimulationsJournal of the American Statistical Association, 113
-
S. Castruccio, M. Genton, Ying Sun (2019)
Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic viewJournal of the Royal Statistical Society: Series A (Statistics in Society), 182
-
Jerome Friedman, Trevor Hastie, R. Tibshirani (2008)
Sparse inverse covariance estimation with the graphical lasso.Biostatistics, 9 3
-
M. Edwards, S. Castruccio, D. Hammerling (2019)
A Multivariate Global Spatiotemporal Stochastic Generator for Climate EnsemblesJournal of Agricultural, Biological and Environmental Statistics
-
D. Vuuren, Jae Edmonds, M. Kainuma, K. Riahi, Allison Thomson, K. Hibbard, G. Hurtt, T. Kram, V. Krey, J. Lamarque, Toshihiko Masui, M. Meinshausen, N. Nakicenovic, Steven Smith, Steven Rose (2011)
The representative concentration pathways: an overviewClimatic Change, 109
-
M. Edwards, S. Castruccio, D. Hammerling (2018)
Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimationComput. Stat. Data Anal., 151
-
A. Baker, D. Hammerling, S. Mickelson, Haiying Xu, Martin Stolpe, P. Naveau, B. Sanderson, I. Ebert‐Uphoff, S. Samarasinghe, F. Simone, F. Carbone, C. Gencarelli, J. Dennis, J. Kay, Peter Lindstrom (2016)
Evaluating lossy data compression on climate simulation data within a large ensembleGeoscientific Model Development, 9
-
A. Poppick, J. Nardi, Noah Feldman, A. Baker, Alexander Pinard, D. Hammerling (2020)
A statistical analysis of lossily compressed climate model dataComput. Geosci., 145
-
A. Baker, Haiying Xu, J. Dennis, M. Levy, D. Nychka, S. Mickelson, Jim Edwards, M. Vertenstein, Al Wegener (2014)
A methodology for evaluating the impact of data compression on climate simulation data -
M. Kennedy, A. O’Hagan (2001)
Bayesian calibration of computer modelsJournal of the Royal Statistical Society: Series B (Statistical Methodology), 63
- Publisher
- Springer Journals
- Copyright
- Copyright © International Biometric Society 2023
- ISSN
- 1085-7117
- eISSN
- 1537-2693
- DOI
- 10.1007/s13253-023-00542-5
- Publisher site
- See Article on Publisher Site
Abstract
We thank all the discussants for their valuable comments. Throughout this rejoinder, we denote the discussants by D = Datta, P = Poppick, BA = Banerjee, BU = Burr, BUD = Bessac, Underwood and Di. We will also use the same acronyms as in the discussion, i.e., VAE = Variational Autoencoders and DNN = Deep Neural Networks. We organize the rejoinder around four main themes.
Journal
Journal of Agricultural Biological and Environmental Statistics – Springer Journals
Published: Jun 1, 2023