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A restricted Liu estimator for binary regression models and its application to an applied demand system
Jönköping University, Jönköping International Business School, JIBS, Statistics.
Florida International University.
Jönköping University, Jönköping International Business School, JIBS, Statistics.
2016 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, 1119-1127 p.Article in journal (Refereed) Published
Abstract [en]

In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by =r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 43, no 6, 1119-1127 p.
Keyword [en]
Liu estimator, logit model, MLE, multicollinearity, MSE, simulation study
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hj:diva-27868DOI: 10.1080/02664763.2015.1092110ISI: 000371182400009Scopus ID: 2-s2.0-84958944234Local ID: IHHStatistikISOAI: oai:DiVA.org:hj-27868DiVA: diva2:851562
Available from: 2015-09-06 Created: 2015-09-06 Last updated: 2017-04-24Bibliographically approved

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Citation style
  • apa
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Language
  • de-DE
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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