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Considering a non-polynomial basis for local kernel regression problem


Silalahi, Divo Dharma and Midi, Habshah (2016) Considering a non-polynomial basis for local kernel regression problem. In: 2nd International Conference and Workshop on Mathematical Analysis (ICWOMA 2016), 2-4 Aug. 2016, Langkawi, Malaysia. (pp. 1-8).


A common used as solution for local kernel nonparametric regression problem is given using polynomial regression. In this study, we demonstrated the estimator and properties using maximum likelihood estimator for a non-polynomial basis such B-spline to replacing the polynomial basis. This estimator allows for flexibility in the selection of a bandwidth and a knot. The best estimator was selected by finding an optimal bandwidth and knot through minimizing the famous generalized validation function.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Institute for Mathematical Research
DOI Number: https://doi.org/10.1063/1.4972168
Publisher: AIP Publishing
Keywords: Generalized cross validation; Kernel nonparametric; Maximum likelihood estimator; Non-polynomial; Regression
Depositing User: Nabilah Mustapa
Date Deposited: 26 Sep 2017 04:04
Last Modified: 26 Sep 2017 04:04
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.4972168
URI: http://psasir.upm.edu.my/id/eprint/57323
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