Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Silalahi, Divo Dharma, Midi, Habshah
Format: Conference or Workshop Item
Language:English
Published: AIP Publishing 2016
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/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.57323
record_format eprints
spelling 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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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
spellingShingle Silalahi, Divo Dharma
Midi, Habshah
Considering a non-polynomial basis for local kernel regression problem
author_facet Silalahi, Divo Dharma
Midi, Habshah
author_sort 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/
_version_ 1643836453146329088
score 13.18916

Similar Items