Evidence map›Paper›PMID 28083872›Full record

ArticleBiometrics2017

A space-time skew-t model for threshold exceedances.

Samuel A Morris, Brian J Reich, Emeric Thibaud, Daniel Cooley

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Samuel A MorrisDepartment of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.
Brian J ReichDepartment of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.
Emeric ThibaudDepartment of Statistics, Colorado State University, Fort Collins, Colorado, U.S.A.
Daniel CooleyDepartment of Statistics, Colorado State University, Fort Collins, Colorado, U.S.A.

Funding

Statistical Methods for Exposure Uncertainty in Air Pollution and Health StudiesR21ES022795 · NIEHS · EMORY UNIVERSITY · PI CHANG, HOWARD H · 2014 to 2015
$442k
NIEHS NIH HHS R21 ES022795
6 · The paper itself

Abstract

To assess the compliance of air quality regulations, the Environmental Protection Agency (EPA) must know if a site exceeds a pre-specified level. In the case of ozone, the level for compliance is fixed at 75 parts per billion, which is high, but not extreme at all locations. We present a new space-time model for threshold exceedances based on the skew-t process. Our method incorporates a random partition to permit long-distance asymptotic independence while allowing for sites that are near one another to be asymptotically dependent, and we incorporate thresholding to allow the tails of the data to speak for themselves. We also introduce a transformed AR(1) time-series to allow for temporal dependence. Finally, our model allows for high-dimensional Bayesian inference that is comparable in computation time to traditional geostatistical methods for large data sets. We apply our method to an ozone analysis for July 2005, and find that our model improves over both Gaussian and max-stable methods in terms of predicting exceedances of a high level.

Indexed as

Models, StatisticalAir PollutantsBayes TheoremNormal DistributionOzoneUnited StatesUnited States Environmental Protection AgencyAir PollutantsOzoneExtreme value analysisMarkov chain Monte CarloRandom partitionSkew-t sepSpatio-temporal modeling

Identifiers

PMID28083872
PMCPMC5507762

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.