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

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 vec...

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Detalles Bibliográficos
Autores principales: Henzi, Alexander, Mösching, Alexandre, Dümbgen, Lutz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810588/
https://www.ncbi.nlm.nih.gov/pubmed/36619375
http://dx.doi.org/10.1007/s11009-022-09937-2
Descripción
Sumario: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.

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