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Accelerating the Pool-Adjacent-Violators Algorithm for Isotonic Distributional Regression

Author

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  • Alexander Henzi

    (University of Bern)

  • Alexandre Mösching

    (Georg-August-University of Göttingen)

  • Lutz Dümbgen

    (University of Bern)

Abstract

In the context of estimating stochastically ordered distribution functions, the pool-adjacent-violators algorithm (PAVA) can be modified such that the computation times are reduced substantially. This is achieved by studying the dependence of antitonic weighted least squares fits on the response vector to be approximated.

Suggested Citation

  • Alexander Henzi & Alexandre Mösching & Lutz Dümbgen, 2022. "Accelerating the Pool-Adjacent-Violators Algorithm for Isotonic Distributional Regression," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2633-2645, December.
    • Handle: RePEc:spr:metcap:v:24:y:2022:i:4:d:10.1007_s11009-022-09937-2
    DOI: 10.1007/s11009-022-09937-2
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    References listed on IDEAS

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    1. Alexander Henzi & Johanna F. Ziegel & Tilmann Gneiting, 2021. "Isotonic distributional regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 963-993, November.
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    Cited by:

    1. Arkadiusz Lipiecki & Bartosz Uniejewski & Rafa{l} Weron, 2024. "Postprocessing of point predictions for probabilistic forecasting of electricity prices: Diversity matters," Papers 2404.02270, arXiv.org.

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