Considering a non-polynomial basis for local kernel regression problem
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...
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2016
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Online Access: | http://psasir.upm.edu.my/id/eprint/57323/1/Considering%20a%20non-polynomial%20basis%20for%20local%20kernel%20regression%20problem.pdf http://psasir.upm.edu.my/id/eprint/57323/ |
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my.upm.eprints.573232017-09-26T04:04:41Z http://psasir.upm.edu.my/id/eprint/57323/ Considering a non-polynomial basis for local kernel regression problem Silalahi, Divo Dharma Midi, Habshah 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. AIP Publishing 2016 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57323/1/Considering%20a%20non-polynomial%20basis%20for%20local%20kernel%20regression%20problem.pdf 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). 10.1063/1.4972168 |
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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. |
format |
Conference or Workshop Item |
author |
Silalahi, Divo Dharma Midi, Habshah |
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Silalahi, Divo Dharma Midi, Habshah Considering a non-polynomial basis for local kernel regression problem |
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Silalahi, Divo Dharma Midi, Habshah |
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Silalahi, Divo Dharma |
title |
Considering a non-polynomial basis for local kernel regression problem |
title_short |
Considering a non-polynomial basis for local kernel regression problem |
title_full |
Considering a non-polynomial basis for local kernel regression problem |
title_fullStr |
Considering a non-polynomial basis for local kernel regression problem |
title_full_unstemmed |
Considering a non-polynomial basis for local kernel regression problem |
title_sort |
considering a non-polynomial basis for local kernel regression problem |
publisher |
AIP Publishing |
publishDate |
2016 |
url |
http://psasir.upm.edu.my/id/eprint/57323/1/Considering%20a%20non-polynomial%20basis%20for%20local%20kernel%20regression%20problem.pdf http://psasir.upm.edu.my/id/eprint/57323/ |
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1643836453146329088 |
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13.18916 |