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arxiv: 2604.18840 · v1 · submitted 2026-04-20 · 📊 stat.AP · stat.CO· stat.ME

Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States

Ben Seiyon Lee , Reetam Majumder , Jordan Richards , Emma S. Simpson , Likun Zhang This is my paper

Pith reviewed 2026-05-10 02:46 UTC · model grok-4.3

classification 📊 stat.AP stat.COstat.ME
keywords spatial extremesBayesian inferencescale mixture processamortized learningsurface skin temperatureheat riskFour Cornersstatistical modeling
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The pith

A random scale mixture process enables scalable Bayesian inference for spatial extremes in US surface temperatures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a statistical model to handle how extreme temperature events depend on one another across many locations in space. Standard approaches for this become too slow when the number of sites grows large, as in modern climate datasets. The authors introduce a random scale mixture process and pair it with advances in spatial statistics and amortized learning to make full Bayesian analysis practical. They test the approach with large simulation studies and then apply it to high-resolution surface skin temperature records from the Four Corners region to map heat risks. Accurate spatial models of this kind matter because they support better public health planning for heat in areas with varied terrain.

Core claim

The authors claim that the random scale mixture process, together with scalable inference strategies that leverage spatial modeling and amortized learning, makes Bayesian inference feasible for spatial extremes, as shown by extensive simulation studies and an application to high-resolution surface skin temperature data in the Four Corners region of the United States.

What carries the argument

The random scale mixture process, a model that represents complex joint tail dependencies among extreme values observed at many spatial locations.

If this is right

  • Bayesian modeling of spatial extremes becomes feasible for datasets containing thousands of locations.
  • Spatially varying and seasonally changing heat extremes can be characterized in regions with complex terrain.
  • Surface skin temperature can be used to derive location-specific heat indices that inform public health risk.
  • Practitioners in climate science and environmental risk assessment gain practical guidelines for large-scale extreme value analysis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mixture structure could be applied to other spatial extremes such as heavy rainfall or strong winds.
  • Amortized components might allow the model to update quickly when new temperature observations arrive.
  • Extending the method across the full United States could identify national patterns in heat risk that local studies miss.
  • Direct comparison of the inferred extremes against output from physical climate models would test consistency between statistical and process-based views.

Load-bearing premise

The random scale mixture process and the amortized learning approximations together capture the actual joint tail dependencies in the temperature data without introducing bias or artifacts that change the heat risk conclusions.

What would settle it

On a smaller data subset where exact but slow Bayesian inference is still possible, the scalable method produces noticeably different estimates of extreme quantiles or risk measures than the exact version.

Figures

Figures reproduced from arXiv: 2604.18840 by Ben Seiyon Lee, Emma S. Simpson, Jordan Richards, Likun Zhang, Reetam Majumder.

Figure 1
Figure 1. Figure 1: Left: Location of the study region within the broader south-central and south￾western United States. The colored raster cells mark the NLDAS grid points over the Four Corners region and their elevations. Middle: Spatially averaged seasonal maximum sur￾face skin temperature (Tskin) over 1979–2024. For each year and season (DJF, MAM, JJA, SON), seasonal block maxima are computed at each grid location and the… view at source ↗
Figure 2
Figure 2. Figure 2: Seasonal maps of the maximum a posteriori (MAP) estimate of the marginal GEV shape field {ξ(s)} over the study region for each season. The white points denote the held-out NLDAS grid cells excluded from model fitting. The four triangle markers identify the GHCN stations—Winslow, Gallup, Albuquerque, and Santa Fe—used for subsequent station-based validation. State boundaries are overlaid for geographic refe… view at source ↗
Figure 3
Figure 3. Figure 3: Spatial maps of seasonal maxima for the Four Corners region in 2024. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Schematic of the modeling framework linking the latent L´evy random scale [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Low-dimensional study. Comparison of parameter estimation accuracy and computational cost across inference methods under four representative simulation settings. Here, Vec(m) denotes the Vecchia approximation with conditioning set size m, Tap(p%) denotes covariance tapering with sparsity level p%, and LR denotes the low-rank method. Boxplots show posterior median estimates of the dependence parameter α obt… view at source ↗
Figure 6
Figure 6. Figure 6: High-dimensional study. Comparison of parameter estimation accuracy and computational cost across inference methods under four representative simulation settings. Boxplots show posterior median estimates of the dependence parameter α obtained from 100 simulated datasets, with the true parameter value (dashed red line). Blue crosses denote the walltime (in minutes) required for each method. Panels represent… view at source ↗
Figure 7
Figure 7. Figure 7: Estimated χu(h) extremal dependence curves for the NLDAS Tskin data, at spatial lag h = 0.177 (∼ 75 km). Results are shown for the four seasons (DJF, MAM, JJA, SON). The left and right panels correspond to Mat´ern covariance functions with smoothness parameters ν = 0.5 and ν = 1.5, respectively. Modeling approaches include the full Gaussian process (Full GP), the Vecchia approximation with conditioning set… view at source ↗
Figure 8
Figure 8. Figure 8: Top row: Observed (from GHCN) and predicted summer maximum 2-m air temperature at four validation sites. The LRSM-based predictions are obtained by first fitting the LRSM to Tskin, then propagating the resulting posterior predictions of Tskin through the trained XGBoost calibration model to obtain T2m. Bottom row: Signed pre￾diction errors (predicted minus observed), where negative values indicate underest… view at source ↗
Figure 9
Figure 9. Figure 9: Posterior median fields of Tskin, T2m, and heat index (HI), and exceedance prob￾abilities Pr(HI > 32◦C) for 2000. Rows show inference methods: Full GP (top), Vec￾chia (m = 5; middle), and covariance tapering (20% sparsity; bottom). The threshold Pr(HI > 32◦C) corresponds to the National Weather Service “extreme caution” category. Supplementary Figure S.15.1 displays pointwise 95% posterior credible interva… view at source ↗
Figure 10
Figure 10. Figure 10: Heat risk and socioeconomic vulnerability in the Four Corners region. Median [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
read the original abstract

Understanding and mapping extreme heat is critical for risk management and public health planning, particularly in regions with complex terrain and heterogeneous climate. We present a case study of extreme heat in the Four Corners region of the United States, using high-resolution surface skin temperature data from the North American Land Data Assimilation System to characterize spatially heterogeneous and seasonally varying extremes across complex terrain, and to assess their implications for heat-related public health risks. Spatial extremes exhibit complex dependencies across geographic regions, which require sophisticated statistical models to capture. While recent advances in spatial extreme value modeling provide flexible representations of joint tail dependencies, statistical inference remains computationally demanding, especially for datasets with a large number of locations. To address this, we propose a random scale mixture process that facilitates Bayesian inference of spatial extremes, and develop scalable inference strategies that leverage advances in spatial modeling and amortized learning. We evaluate the proposed inference methods through large-scale simulation studies, representing the first such extensive study in spatial extremes, and a high-resolution surface skin temperature application in the Four Corners region. Surface skin temperature is particularly useful as a predictor for air temperature, for studying heatwaves and related environmental phenomena, and to calculate heat indices reflecting downstream health risks at any location. Our findings provide insights into efficient, data-driven approaches for modeling spatial extremes, and serve as guidelines for practitioners in the fields of climate science, environmental risk assessment, and beyond.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes a random scale mixture process to enable Bayesian inference for spatial extreme value models, develops scalable inference strategies that combine advances in spatial modeling with amortized learning, evaluates the methods via large-scale simulation studies (described as the first extensive study of its kind), and applies the framework to high-resolution North American Land Data Assimilation System surface skin temperature data over the Four Corners region to characterize spatially heterogeneous extremes and assess implications for heat-related public health risks.

Significance. If the proposed process and inference strategies prove accurate and scalable, the work would address a recognized computational barrier in spatial extremes modeling, enabling Bayesian analyses at scales relevant to climate and environmental applications. The emphasis on extensive simulations and a concrete public-health case study strengthens practical utility and could provide useful guidelines for practitioners in climate science and risk assessment.

minor comments (2)
  1. The abstract would be strengthened by including one or two quantitative highlights from the simulation studies (e.g., runtime reductions or coverage probabilities) to substantiate the scalability claims.
  2. Clarify in the methods or results whether the random scale mixture process introduces any additional assumptions on tail dependence that are not directly validated against the surface skin temperature data.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript, accurate summary of the proposed random scale mixture process and amortized inference strategy, and recommendation for minor revision. We are pleased that the significance for addressing computational barriers in spatial extremes modeling, the extensive simulation studies, and the public-health application to heat risk are recognized.

Circularity Check

0 steps flagged

No significant circularity; proposal is a new modeling framework evaluated externally

full rationale

The paper introduces a random scale mixture process and amortized inference strategies motivated by computational limitations of existing spatial extremes methods. It supports these via large-scale simulation studies (described as the first of their kind) and a real-data application to Four Corners surface skin temperature, without any equations or claims reducing predictions to fitted parameters by construction. No self-citation chains, uniqueness theorems, or ansatzes are invoked as load-bearing justifications for the core results. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit equations or implementation details, preventing identification of specific free parameters, axioms, or invented entities; the model is described at a high level only.

pith-pipeline@v0.9.0 · 5567 in / 1068 out tokens · 35988 ms · 2026-05-10T02:46:39.480780+00:00 · methodology

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Reference graph

Works this paper leans on

176 extracted references · 176 canonical work pages

  1. [1]

    and Williams, A

    Abatzoglou, John T. and Williams, A. Park , title =. Proc. Natl. Acad. Sci. U.S.A. , year =

    work page

  2. [2]

    Methods to calculate the heat index as an exposure metric in environmental health research , author=. Environ. Health Persp. , volume=

    work page

  3. [3]

    Environmetrics , volume=

    Hu, Chenglei and Baltazar Bispo, Regina and Rue, H. Environmetrics , volume=. 2026 , publisher=

    work page 2026

  4. [4]

    Integral representations for

    Askey, Richard and Fitch, James , journal=. Integral representations for

    work page

  5. [5]

    Baddeley, Adrian and Rubak, Ege and Turner, Rolf , year=

    work page

  6. [6]

    Banerjee, Sudipto and Carlin, Bradley P and Gelfand, Alan E , year=

    work page

  7. [7]

    Flux parameterization over land surfaces for atmospheric models , author=. J. Appl. Meteorol. Clim. , volume=

    work page

  8. [8]

    Berg, Christian and Mateu, Jorge and Porcu, Emilio , journal=. The. 2008 , publisher=

    work page 2008

  9. [9]

    Julia: A fresh approach to numerical computing , author=. SIAM. 2017 , publisher=

    work page 2017

  10. [10]

    The Annals of Applied Statistics , pages=

    Spatial modeling of extreme snow depth , author=. The Annals of Applied Statistics , pages=. 2011 , volume=

    work page 2011

  11. [11]

    Spatially adaptive covariance tapering , author=. Spat. Stat. , volume=. 2016 , publisher=

    work page 2016

  12. [12]

    High--order composite likelihood inference for max--stable distributions and processes , author =. J. Comput. Graph. Stat. , volume =

    work page

  13. [13]

    Bayesian spatial modeling of extreme precipitation return levels , author=. J. Am. Stat. Assoc. , volume=. 2007 , publisher=

    work page 2007

  14. [14]

    and Johannesson, G

    Cressie, N. and Johannesson, G. , date-added =. J. R. Stat. Soc. B. , number =

    work page

  15. [15]

    Basis-function models in spatial statistics , author=. Annu. Rev. Stat. Appl. , volume=. 2022 , publisher=

    work page 2022

  16. [16]

    Cressie, Noel and Wikle, Christopher K , year=

    work page

  17. [17]

    Geostatistics of dependent and asymptotically independent extremes , author=. Math. Geosci. , volume=. 2013 , publisher=

    work page 2013

  18. [18]

    A spectral representation for max-stable processes , author=. Ann. Probab. , pages=. 1984 , volume=

    work page 1984

  19. [19]

    Flexible space--time models for extreme data , author=. Spat. Stat. , volume=. 2025 , publisher=

    work page 2025

  20. [20]

    Climate change and changes in global precipitation patterns:

    Dore, Mohammed H I , journal=. Climate change and changes in global precipitation patterns:. 2005 , publisher=

    work page 2005

  21. [21]

    Weather Clim

    Detection and attribution of climate extremes in the observed record , author=. Weather Clim. Extrem. , volume=. 2016 , publisher=

    work page 2016

  22. [22]

    and Capon, Anthony and Berry, Peter and Broderick, Carolyn and de Dear, Richard and Havenith, George and Honda, Yasushi and Kovats, R

    Ebi, Kristie L. and Capon, Anthony and Berry, Peter and Broderick, Carolyn and de Dear, Richard and Havenith, George and Honda, Yasushi and Kovats, R. Sari and Ma, Wei and Malik, Arunima and Morris, Nathan B. and Nybo, Lars and Seneviratne, Sonia I. and Vanos, Jennifer and Jay, Ollie , title=. The Lancet , year=

    work page

  23. [23]

    Evaluating station, satellite, & combined data for

    Sch. Evaluating station, satellite, & combined data for. Atmos. Res. , pages=. 2025 , publisher=

    work page 2025

  24. [24]

    Extremes , volume=

    Gradient boosting with extreme-value theory for wildfire prediction , author=. Extremes , volume=. 2023 , publisher=

    work page 2023

  25. [25]

    Extremes , volume=

    Gradient boosting for extreme quantile regression , author=. Extremes , volume=. 2023 , publisher=

    work page 2023

  26. [26]

    Extremes , volume=

    Extremal dependence of random scale constructions , author=. Extremes , volume=. 2019 , publisher=

    work page 2019

  27. [27]

    Covariance tapering for interpolation of large spatial datasets , author=. J. Comput. Graph. Stat. , volume=. 2006 , publisher=

    work page 2006

  28. [28]

    Journal of Multivariate Analysis , volume=

    Compactly supported correlation functions , author=. Journal of Multivariate Analysis , volume=. 2002 , publisher=

    work page 2002

  29. [29]

    Stochastic models that separate fractal dimension and the

    Gneiting, Tilmann , journal=. Stochastic models that separate fractal dimension and the. 2004 , publisher=

    work page 2004

  30. [30]

    Strictly Proper Scoring Rules, Prediction, and Estimation , author=. J. Am. Stat. Assoc. , volume=

    work page

  31. [31]

    Technometrics , volume =

    Joseph Guinness , title =. Technometrics , volume =

    work page

  32. [32]

    and Schramm, Paul J

    Hayden, Mary H. and Schramm, Paul J. and Beard, Charles B. and Bell, Jesse E. and Bernstein, Aaron S. and Bieniek-Tobasco, Ashley and Cooley, Nikki and Diuk-Wasser, Maria and Michael K. Dorsey and Ebi, Kristie L. and Ernst, Kacey C. and Gorris, Morgan E. and Howe, Peter D. and Khan, Ali S. and Lefthand-Begay, Clarita and Maldonado, Julie and Saha, Shubhay...

    work page

  33. [33]

    A case study competition among methods for analyzing large spatial data , author=. J. Agr. Biol. Envir. St. , volume=. 2019 , publisher=

    work page 2019

  34. [34]

    Space--time modelling of extreme events , author =. J. R. Stat. Soc. B. , volume =

    work page

  35. [35]

    Stat , volume =

    Full likelihood inference for max--stable data , author =. Stat , volume =

    work page

  36. [36]

    Bridging asymptotic independence and dependence in spatial extremes using

    Huser, Rapha. Bridging asymptotic independence and dependence in spatial extremes using. Spat. Stat. , volume=. 2017 , publisher=

    work page 2017

  37. [37]

    Modeling of spatial extremes in environmental data science: Time to move away from max--stable processes , author=. Environ. Data Sci. , volume=. 2025 , publisher=

    work page 2025

  38. [38]

    Stein and Peng Zhong , title =

    Raphaël Huser and Michael L. Stein and Peng Zhong , title =. J. Comput. Graph. Stat. , volume =

    work page

  39. [39]

    Modeling spatial processes with unknown extremal dependence class , author=. J. Am. Stat. Assoc. , volume=. 2019 , publisher=

    work page 2019

  40. [40]

    WIREs Comput

    Advances in statistical modeling of spatial extremes , author=. WIREs Comput. Stat. , volume=. 2022 , publisher=

    work page 2022

  41. [41]

    Land surface skin temperature climatology: Benefitting from the strengths of satellite observations , author=. Environ. Res. Lett. , volume=. 2010 , publisher=

    work page 2010

  42. [42]

    Fixed--width output analysis for

    Jones, Galin L and Haran, Murali and Caffo, Brian S and Neath, Ronald , journal=. Fixed--width output analysis for. 2006 , publisher=

    work page 2006

  43. [43]

    Stationary max--stable fields associated to negative definite functions , author =. Ann. Probab. , volume =

    work page

  44. [44]

    Stat , volume=

    Spatial extremal modelling: A case study on the interplay between margins and dependence , author=. Stat , volume=. 2024 , publisher=

    work page 2024

  45. [45]

    Vecchia approximations of

    Katzfuss, Matthias and Guinness, Joseph and Gong, Wenlong and Zilber, Daniel , journal=. Vecchia approximations of. 2020 , publisher=

    work page 2020

  46. [46]

    Keetch, John James and Byram, George Marsden , year=

    work page

  47. [47]

    Space--time extremes of severe

    Koh, Jonathan and Koch, Erwan and Davison, Anthony C , journal=. Space--time extremes of severe. 2024 , volume=

    work page 2024

  48. [48]

    Factor copula models for replicated spatial data , author=. J. Am. Stat. Assoc. , volume=. 2018 , publisher=

    work page 2018

  49. [49]

    Hydrolog

    The implications of projected climate change for freshwater resources and their management , author=. Hydrolog. Sci. J. , volume=. 2008 , publisher=

    work page 2008

  50. [50]

    Biometrika , volume =

    Statistics for near independence in multivariate extreme values , author =. Biometrika , volume =

    work page

  51. [51]

    Leeper and Tyler Harrington and Michael A

    Ronald D. Leeper and Tyler Harrington and Michael A. Palecki and Kelley DePolt and Emma Scott and Jennifer Runkle and Howard J. Diamond. The Influence of Drought on Heat Wave Intensity, Duration, and Exposure. J. Appl. Meteorol. Clim. 2025. doi:10.1175/JAMC-D-24-0072.1

    work page doi:10.1175/jamc-d-24-0072.1 2025

  52. [52]

    Lehmann, Erich L and Casella, George , year=

    work page

  53. [53]

    Neural networks for parameter estimation in intractable models , author=. Comp. Stat. Data An. , volume=. 2023 , publisher=

    work page 2023

  54. [54]

    and Rowhani, P

    Lesk, C. and Rowhani, P. and Ramankutty, N. , title =. Nature , year =

    work page

  55. [55]

    Chapter 13: Transportation

    Liban, Cris B and Kafalenos, Robert and Alessa, Lilian and Anenberg, Susan and Chester, Mikhail and DeFlorio, Joshua and D \'o \ n ez, Francisco J and Flannery, Aimee and Sanio, Michael R and Scott, Beverly A and Stoner, Anne Marie K. Chapter 13: Transportation

    work page

  56. [56]

    An explicit link between

    Lindgren, Finn and Rue, H. An explicit link between. J. R. Stat. Soc. B. , year =

    work page

  57. [57]

    2023 , comment =

    A deep learning synthetic likelihood approximation of a non--stationary spatial model for extreme streamflow forecasting , journal =. 2023 , comment =. doi:https://doi.org/10.1016/j.spasta.2023.100755 , author =

    work page doi:10.1016/j.spasta.2023.100755 2023

  58. [58]

    Modeling extremal streamflow using deep learning approximations and a flexible spatial process , volume =

    Majumder, Reetam and Reich, Brian J and Shaby, Benjamin A , journal =. Modeling extremal streamflow using deep learning approximations and a flexible spatial process , volume =

    work page

  59. [59]

    Chapter 2: Climate Trends

    Marvel, Kate and Su, Wenying and Delgado, Roberto and Aarons, Sarah and Chatterjee, Abhishek and Garcia, Margaret E and Hausfather, Zeke and Hayhoe, Katharine and Hence, Deanna A and Jewett, Elizabeth B and Robel, Alexander and Singh, Deepti and Tripati, Aradhna and Vose, Russell S. Chapter 2: Climate Trends

    work page

  60. [60]

    Management Science , volume=

    Scoring rules for continuous probability distributions , author=. Management Science , volume=. 1976 , publisher=

    work page 1976

  61. [61]

    The Annals of Statistics , volume=

    What is a statistical model?\ (with discussion) , author=. The Annals of Statistics , volume=. 2002 , publisher=

    work page 2002

  62. [62]

    Bernoulli , volume=

    Polynomial covariance functions on intervals , author=. Bernoulli , volume=. 2003 , publisher=

    work page 2003

  63. [63]

    Weather-Related Fatality and Injury Statistics , year =

    work page

  64. [64]

    A low-to-no snow future and its impacts on water resources in the western

    Siirila-Woodburn, Erica R and Rhoades, Alan M and Hatchett, Benjamin J and Huning, Laurie S and Szinai, Julia and Tague, Christina and Nico, Peter S and Feldman, Daniel R and Jones, Andrew D and Collins, William D and others , journal=. A low-to-no snow future and its impacts on water resources in the western. 2021 , publisher=

    work page 2021

  65. [65]

    Nolan, John P , publisher=

    work page

  66. [66]

    Terminology in thermal infrared remote sensing of natural surfaces , author=. Agr. Forest Meteorol. , volume=. 1995 , publisher=

    work page 1995

  67. [67]

    Statistical post--processing of forecasts for extremes using bivariate

    Oesting, Marco and Schlather, Martin and Friederichs, Petra , journal=. Statistical post--processing of forecasts for extremes using bivariate. 2017 , publisher=

    work page 2017

  68. [68]

    Remotely sensed land skin temperature as a spatial predictor of air temperature across the conterminous

    Oyler, Jared W and Dobrowski, Solomon Z and Holden, Zachary A and Running, Steven W , journal=. Remotely sensed land skin temperature as a spatial predictor of air temperature across the conterminous

    work page

  69. [69]

    Likelihood--based inference for max-stable processes , author=. J. Am. Stat. Assoc. , volume=. 2010 , publisher=

    work page 2010

  70. [70]

    and Lewis, Sophie C

    Perkins-Kirkpatrick, Sarah E. and Lewis, Sophie C. , title =. Nature Communications , year =

    work page

  71. [71]

    Asymptotic properties of the maximum in a stationary

    Pickands, James , journal=. Asymptotic properties of the maximum in a stationary

    work page

  72. [72]

    Environmetrics , volume=

    Fast parameter estimation of generalized extreme value distribution using neural networks , author=. Environmetrics , volume=. 2024 , publisher=

    work page 2024

  73. [73]

    The Annals of Applied Statistics , volume=

    A hierarchical max--stable spatial model for extreme precipitation , author=. The Annals of Applied Statistics , volume=. 2012 , publisher=

    work page 2012

  74. [74]

    , year =

    Resnick, Sidney I. , year =

    work page

  75. [75]

    Journal de la Soci

    Spatial extremes: Max-stable processes at work , author=. Journal de la Soci

    work page

  76. [76]

    Statistica Sinica , volume=

    Bayesian inference from composite likelihoods, with an application to spatial extremes , author=. Statistica Sinica , volume=. 2012 , publisher=

    work page 2012

  77. [77]

    evgam: An

    Youngman, Benjamin D , journal=. evgam: An

    work page

  78. [78]

    and Huser, R

    Richards, J. and Huser, R. , editor =. Extreme Quantile Regression with Deep Learning , booktitle =

    work page

  79. [79]

    Flexible modeling of nonstationary extremal dependence using spatially fused

    Shao, Xuanjie and Hazra, Arnab and Richards, Jordan and Huser, Rapha. Flexible modeling of nonstationary extremal dependence using spatially fused. Technometrics , volume=. 2025 , publisher=

    work page 2025

  80. [80]

    Biometrika , pages=

    Dependence modelling for spatial extremes , author=. Biometrika , pages=. 2012 , publisher=

    work page 2012

Showing first 80 references.