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Quantile regression estimation of nonlinear longitudinal data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science, Statistics.ORCID iD: 0000-0003-3691-8326
2004 (English)Report (Other academic)
Abstract [en]

This paper examines a weighted version of the quantile regression estimator defined by Koenker and Bassett (1978), adjusted to the case of nonlinear longitudinal data. Different weights are used and compared by computer simulation using a four-parameter logistic growth function and error terms following an AR(1) model. It is found that the estimator is performing quite well, especially for the median regression case, that the differences between the weights are small, and that increasing the correlation in the AR(1) model leads to better behaviour of the estimator. A comparison is made with the corresponding mean regression estimator, which is found to be less robust. Finally the estimator is applied to a data set with growth patterns of two genotypes of soybean.

Place, publisher, year, edition, pages
Uppsala: Institutionen för informationsvetenskap , 2004. , p. 27
Series
Research report / Uppsala University, Department of Information Science, ISSN 1403-7572 ; 2004:5
Keyword [en]
quantile regression, longitudinal data, nonlinear modell, Monte Carlo simulation
Identifiers
URN: urn:nbn:se:uu:diva-4831OAI: oai:DiVA.org:uu-4831DiVA, id: diva2:129357
Available from: 2005-03-01 Created: 2005-03-01 Last updated: 2013-06-20

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf