Author
Listed:
- Farzaneh Boroumand
(Macquarie University, School of Mathematical and Physical Sciences
Health School, Mashhad University of Medical Sciences, Department of Biostatistics
Faculty of Medicine and Health, University of Sydney, Sydney School of Public Health)
- Mohammad T. Shakeri
(Health School, Mashhad University of Medical Sciences, Department of Biostatistics)
- Nino Kordzakhia
(Macquarie University, School of Mathematical and Physical Sciences)
- Mahdi Salehi
(University of Neyshabur, Department of Mathematics and Statistics
University of Pretoria, Department of Statistics)
- Hassan Doosti
(Macquarie University, School of Mathematical and Physical Sciences)
Abstract
This paper provides the theory about the convergence rate of the tilted version of linear smoother. We study tilted linear smoother, a class of nonparametric regression function estimators, which is obtained by minimizing the distance to an infinite order flat-top trapezoidal kernel estimator. We prove that the proposed estimator achieves a high level of accuracy. Moreover, it preserves the attractive properties of the infinite order flat-top kernel estimator. We also present an extensive numerical study for analysing the performance of two members of the tilted linear smoother class named tilted Nadaraya-Watson and tilted local linear for finite samples. The simulation study shows that tilted Nadaraya-Watson and tilted local linear perform better than their classical analogs, under some specified conditions, in terms of Median Integrated Squared Error (MISE). Next, the performance of these estimators as well as the conventional estimators are illustrated by curve fitting to COVID-19 data for 12 countries and a dose-response data set. Finally, the R codes for obtaining various regression estimators mentioned above are given as an appendix.
Suggested Citation
Farzaneh Boroumand & Mohammad T. Shakeri & Nino Kordzakhia & Mahdi Salehi & Hassan Doosti, 2024.
"Tilted Nonparametric Regression Function Estimation,"
Springer Books, in: Hassan Doosti (ed.), Flexible Nonparametric Curve Estimation, pages 1-24,
Springer.
Handle:
RePEc:spr:sprchp:978-3-031-66501-1_1
DOI: 10.1007/978-3-031-66501-1_1
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