General rank-based estimation for regression single index models

This study considers rank estimation of the regression coefficients of the single index regression model. Conditions needed for the consistency and asymptotic normality of the proposed estimator are established. Monte Carlo simulation experiments demonstrate the robustness and efficiency of the prop...

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Bibliographic Details
Published in:Annals of the Institute of Statistical Mathematics 2018-10, Vol.70 (5), p.1115-1146
Main Authors: Bindele, Huybrechts F., Abebe, Ash, Meyer, Karlene N.
Format: Article
Language:English
Subjects:
Computer simulation
Economic models
Economics
Finance
Insurance
Management
Mathematics
Mathematics and Statistics
Monte Carlo simulation
Nonparametric statistics
Normality
Regression analysis
Regression coefficients
Regression models
Statistics
Statistics for Business
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Summary:This study considers rank estimation of the regression coefficients of the single index regression model. Conditions needed for the consistency and asymptotic normality of the proposed estimator are established. Monte Carlo simulation experiments demonstrate the robustness and efficiency of the proposed estimator compared to the semiparametric least squares estimator. A real-life example illustrates that the rank regression procedure effectively corrects model nonlinearity even in the presence of outliers in the response space.
ISSN:0020-3157
1572-9052
DOI:10.1007/s10463-017-0618-9