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A generalised PAV algorithm for monotonic regression in several variables
Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .ORCID iD: 0000-0003-1836-4200
Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics.
2004 (English)In: COMPSTAT. Proceedings in Computational Statistics / [ed] J. Antoch, Heidelberg, NY: PhysicaVerlag/Springer , 2004, 761-767 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a new algorithm for monotonic regression in one or more explanatory variables. Formally, our method generalises the well-known PAV (pool-adjacent-violators) algorithm from fully to partially ordered data. The computational complexity of our algorithm is O(n2). The goodness-of-fit to observed data is much closer to optimal than for simple averaging techniques.

Place, publisher, year, edition, pages
Heidelberg, NY: PhysicaVerlag/Springer , 2004. 761-767 p.
Keyword [en]
Statistical computing, numerical algorithms, monotonic regression, nonparametric regression, pool-adjacent-violators algorithm
National Category
Computational Mathematics Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-24329Local ID: 3957ISBN: 3-7908-1554-3 (print)OAI: oai:DiVA.org:liu-24329DiVA: diva2:244647
Conference
16th Symposium in Computational Statistics COMPSTAT (Prague, Czech Republic, 2004)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2015-06-02

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Burdakov, OlegGrimvall, AndersHussian, Mohamed

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