Composite quantile regression for linear errors-in-variables models

Year 2015, Volume: 44 Issue: 3, 707 - 713, 01.06.2015

Rong Jiang

Abstract

Composite quantile regression can be more efficient and sometimes arbitrarily more efficient than least squares for non-normal random errors,
and almost as efficient for normal random errors. Therefore, we extend composite quantile regression method to linear errors-in-variables
models, and prove the asymptotic normality of the proposed estimators. Simulation results and a real dataset are also given to illustrate
our the proposed methods.

Keywords

Errors-in-variables, composite quantile regression

References

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