Abstract Details
Activity Number:
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318
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Type:
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Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #317279
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View Presentation
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Title:
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Aranda-Ordaz Quantile Regression for Student Performance Assessment
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Author(s):
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Mario Cortina-Borja and Hakim-Moulay Dehbi and Marco Geraci*
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Companies:
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University College London and Imperial College London and University of South Carolina
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Keywords:
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bounded variable ;
transformation ;
education ;
Millennium Cohort Study ;
marginal effect
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Abstract:
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In education research, normal regression models may not be appropriate due to the presence of bounded variables, which may exhibit a large variety of distributional shapes and present floor and ceiling effects. Bearing this in mind we develop a class of quantile regression models for bounded response variables. The one-parameter Aranda-Ordaz (AO) symmetric and asymmetric families of transformations are applied to address modelling issues that arise when estimating conditional quantiles of a bounded response variable whose relationship with the covariates is possibly nonlinear. This approach exploits the equivariance property of quantiles and aims at achieving linearity of the predictor. This offers a flexible model-based alternative to nonparametric estimation of the quantile function. Since the transformation is quantile-specific, the modelling takes into account the local features of the conditional distribution of the response variable.
Our study is motivated by the analysis of reading performance in 7-year old children part of the UK Millennium Cohort Study.
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Authors who are presenting talks have a * after their name.
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