Semi-parametric rank regression with missing responses

We consider a semi-parametric regression model with responses missing at random and study the rank estimator of the regression coefficient. Consistency and asymptotic normality of the proposed estimator are established. Monte Carlo simulation experiments show that the proposed estimator is more effi...

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Bibliographic Details
Published in:Journal of multivariate analysis 2015-12, Vol.142, p.117-132
Main Authors: Bindele, Huybrechts F., Abebe, Ash
Format: Article
Language:English
Subjects:
Asymptotic methods
Asymptotic normality
Estimating techniques
Inverse probability
Missing at random
Monte Carlo simulation
Normal distribution
Regression analysis
Simple imputation
Strong consistency
Studies
Wilcoxon estimator
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Summary:We consider a semi-parametric regression model with responses missing at random and study the rank estimator of the regression coefficient. Consistency and asymptotic normality of the proposed estimator are established. Monte Carlo simulation experiments show that the proposed estimator is more efficient than the least squares estimator whenever the error distribution is heavy tailed or contaminated. When the errors follow a normal distribution, these simulation experiments show that the rank estimator can be more efficient than its least squares counterpart for cases with large proportion of missing responses.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2015.08.007