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Predictive Accuracy of Linear Models with Ordinal Regressors
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This paper considers four approaches to ordinal predictors in linear regression to evaluate how these contrast with respect to predictive accuracy. The two most typical treatments, namely, dummy coding and classic linear regression on assigned level scores are compared with two improved methods; penalized smoothed coefficients and a generalized additive model with cubic splines. A simulation study is conducted to assess all on the basis of predictive performance. Our results show that the dummy based methods surpass the numeric at low sample sizes. Although, as sample size increases, differences between the methods diminish. Tendencies of overfitting are identified among the dummy methods. We conclude by stating that the choice of method not only ought to be context driven, but done in the light of all characteristics.

Place, publisher, year, edition, pages
2016. , 24 p.
Keyword [en]
variable classification, predictive performance, model specification, discretized continuous data
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-273958OAI: diva2:895644
Subject / course
Educational program
Freestanding course
Available from: 2016-02-10 Created: 2016-01-19 Last updated: 2016-02-10Bibliographically approved

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Department of Statistics
Probability Theory and Statistics

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ReferencesLink to record
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