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A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes

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Arnab Hazra, Brian J. Reich, Ana-Maria Staicu

Applications · Submitted 31 Dec 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In a numerical study, we find that the proposed model outperforms multivariate spatial Gaussian processes, multivariate spatial t-processes including their univariate alternatives in terms of various model selection criteria.

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arXiv ID1812.11704 (version 1)
Pith IDABHVC32S, Pith's permanent record of this paper and its review
CitationAna-Maria Staicu, Arnab Hazra, Brian J. Reich. "A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes." arXiv:1812.11704 (2018). https://pith.science/paper/ABHVC32S
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,030 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.07 · human participants 0.02 · controlled comparison 0.39 · randomized assignment 0.01 · states quantitative result 0.26 · compares with prior results 0.94 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

To study trends in extreme precipitation across US over the years 1951-2017, we consider 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation, annual maximum of consecutive 5-day average precipitation, which exhibit spatial correlation as well as mutual dependence. We consider the gridded data, produced by the CLIMDEX project (http://www.climdex.org/gewocs.html), constructed using daily precipitation data. In this paper, we propose a multivariate spatial skew-t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. In a numerical study, we find that the proposed model outperforms multivariate spatial Gaussian processes, multivariate spatial t-processes including their univariate alternatives in terms of various model selection criteria. We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 To study trends in extreme precipitation across US over the years 1951-2017, we consider 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation, annual maximum of consecutive 5-day average precipitation, which exhibit spatial correlation as well as mutual dependence. background 0.84 0.59 0.04 asserted 0.92
s1 We consider the gridded data, produced by the CLIMDEX project (http://www.climdex.org/gewocs.html), constructed using daily precipitation data. method 0.89 0.14 0.02 asserted 0.96
s2 In this paper, we propose a multivariate spatial skew-t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. contribution 0.90 0.92 0.12 asserted 0.99
s3 The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. contribution 0.52 0.80 0.07 asserted 0.98
s4 In a numerical study, we find that the proposed model outperforms multivariate spatial Gaussian processes, multivariate spatial t-processes including their univariate alternatives in terms of various model selection criteria. result 1.00 0.97 0.46 asserted 1.00
s5 We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes. result 0.98 0.97 0.43 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.92.

What was examined, and what was not

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  • full text
  • figures and tables
  • references and citing sentences
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

To study trends in extreme precipitation across US over the years 1951-2017, we consider 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation, annual maximum of consecutive 5-day average precipitation, which exhibit spatial correlation as well as mutual dependence. We consider the gridded data, produced by the CLIMDEX project (http://www.climdex.org/gewocs.html), constructed using daily precipitation data. In this paper, we propose a multivariate spatial skew-t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. In a numerical study, we find that the proposed model outperforms multivariate spatial Gaussian processes, multivariate spatial t-processes including their univariate alternatives in terms of various model selection criteria. We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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