Small area quantile estimation via spline regression and empirical likelihood

Section 7. Conclusion

Table of contents

We studied the small area quantile estimation under the nested-error non-parametric regression model and a semi-parametric DRM assumption on error distributions. We proposed two quantile estimators based on P-splines and empirical likelihood approach. Simulation results show that the proposed estimators are robust and have respectable efficiency under both linear and non-parametric regression functions for mid-range quantiles. The proposed approach can be extended to non-parametric regression models with multiple covariates in principle, though it will lead to many more parameters to be estimated. This problem will be investigated in a future work.

Acknowledgements

We thank Professors Simon Wood, Matt Wand, Mahmoud Torabi and Song Cai for their helpful suggestions on R packages used in this paper. This work was supported by the National Natural Science Foundation of China (No.11661067), Natural Science Foundation of Qinghai Province (No.2015-ZJ-717,2019-ZJ-920), “Western Light” talent program of Chinese Academy of Science (2017) and the funding through the Canadian Statistical Sciences Institute.

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Model and assumptions
Section 3. Proposed approach
Section 4. Bootstrap estimation of the mean squared errors
Section 5. Monte Carlo simulations
Section 6. Empirical application
Section 7. Conclusion
Acknowledgements
References

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