Works matching IS 11804009 AND DT 2019 AND VI 30 AND IP 4
Results: 10
Statistics for climate informatics.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2567
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A likelihood for correlated extreme series.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2546
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Estimating precipitation extremes using the log‐histospline.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2543
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A study of links between the Arctic and the midlatitude jet stream using Granger and Pearl causality.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2540
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Unsupervised space–time clustering using persistent homology.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2539
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Double‐structured sparse multitask regression with application of statistical downscaling.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2534
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Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2533
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Increases in the extreme rainfall events: Using the Weibull distribution.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2532
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Predicting climate types for the Continental United States using unsupervised clustering techniques.
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- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2524
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- Article
Issue Information.
- Published in:
- Environmetrics, 2019, v. 30, n. 4, p. N.PAG, doi. 10.1002/env.2514
- Publication type:
- Article