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An O(n2) algorithm for isotonic regression problems
Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.ORCID iD: 0000-0003-1836-4200
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Arts and Sciences.
2006 (English)In: Large-Scale Nonlinear Optimization / [ed] G. Di Pillo and M. Roma, Springer-Verlag , 2006, 25-33 p.Chapter in book (Refereed)
Abstract [en]

Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.

The chapters of the book, authored by some of the most active and well-known researchers in nonlinear optimization, give an updated overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications

Place, publisher, year, edition, pages
Springer-Verlag , 2006. 25-33 p.
Series
Nonconvex Optimization and Its Applications, 83
Keyword [en]
Quadratic programming, large scale optimization, least distance problem, isotonic regression, pool-adjacent-violators algorithm
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-60581ISBN: 978-0-387-30063-4 (print)ISBN: 0-387-3-0065-1 OAI: oai:DiVA.org:liu-60581DiVA: diva2:357983
Available from: 2010-10-20 Created: 2010-10-20 Last updated: 2015-06-02Bibliographically approved
In thesis
1. Monotonic regression for large multivariate datasets
Open this publication in new window or tab >>Monotonic regression for large multivariate datasets
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Monoton regression för stora multivariata datamateriaI
Abstract [en]

Monotonic regression is a non-parametric statistical method that is designed especially for applications in which the expected value of a response variable increases or decreases in one or more explanatory variables. Such applications can be found in business, physics, biology, medicine, signal processing, and other areas. Inasmuch as many of the collected datasets can contain a very large number of multivariate observations, there is a strong need for efficient numerical algorithms. Here, we present new methods that make it feasible to fit monotonic functions to more than one hundred thousand data points. By simulation, we show that our algorithms have high accuracy and represent  considerable improvements with respect to computational time and memory requirements. In particular , we demonstrate how segmentation of a large-scale problem can greatly improve the performance of existing algorithms. Moreover, we show how the uncertainty of a monotonic regression model can be estimated. One of the procedures we developed can be employed to estimate the variance of the random error present in the observed response. Other procedures are based on resampling  techniques and can provide confidence intervals for the expected response at given levels of a set of predictors.

Abstract [sv]

Monoton regression är en icke-parametrisk statistisk metod som är utvecklad speciellt för tillämpningar i vilka det förväntade värdet aven responsvariabel ökar eller minskar med en eller flera förklaringsvariabler. Sådana tillämpningar finns inom företagsekonomi, fysik, biologi, medicin, signalbehandling och andra områden. Eftersom många insamlade datamaterial kan innehålla ett mycket stort antal multivariata observationer finns ett starkt behov av effektiva numeriska algoritmer. Här presenterar vi nya metoder som gör det möjligt att anpassa monotona funktioner till mer än 100000 datapunkter. Genom simulering visar vi. att våra algoritmer har hög noggrannhet och innebär betydande förbättringar med avseende på beräkningstid och krav på minnesutrymme. Speciellt visar vi hur segmentering av ett storskaligt problem starkt kan förbättra existerande algoritmer. Dessutom visar vi hur osäkerheten aven monoton regressions modell kan uppskattas. En av de metoder vi utvecklat kan användas för att uppskatta variansen för de slumpkomponenter som kan finnas i den observerade responsvariabeln. Andra metoder, baserade på s.k. återsampling, kan ge konfidensintervall för den förväntade responsen för givna värden på ett antal prediktorer.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 75 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 11Linköping Studies in Arts and Science, ISSN 0282-9800 ; 514
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-65349 (URN)978-91-7393-412-1 (ISBN)
Public defence
2010-04-16, Glashuset, Building B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Available from: 2011-02-04 Created: 2011-02-04 Last updated: 2012-11-08Bibliographically approved

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Burdakov, OlegSysoev, OlegGrimvall, AndersHussian, Mohammed

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