REGRESI NONPARAMETRIK KERNEL ADJUSTED

Nadaraya watson�s kernel adjusted regression estimator is an estimator that whose kernel is taken from the family of scale-location associated with the classical kernel density estimator. Based on these estimator, it can be obtained optimal bandwith and scale parameter. This estimator gives a bett...

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Main Authors: , NOVITA EKA CHANDRA, , Prof. Dr. Sri Haryatmi, M.Sc.
Format: Theses and Dissertations NonPeerReviewed
Published: [Yogyakarta] : Universitas Gadjah Mada 2014
Subjects:
ETD
Online Access:https://repository.ugm.ac.id/133812/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74649
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Institution: Universitas Gadjah Mada
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spelling id-ugm-repo.1338122016-03-04T08:04:02Z https://repository.ugm.ac.id/133812/ REGRESI NONPARAMETRIK KERNEL ADJUSTED , NOVITA EKA CHANDRA , Prof. Dr. Sri Haryatmi, M.Sc. ETD Nadaraya watson�s kernel adjusted regression estimator is an estimator that whose kernel is taken from the family of scale-location associated with the classical kernel density estimator. Based on these estimator, it can be obtained optimal bandwith and scale parameter. This estimator gives a better estimation results compared with Naradaya Watson�s clasical kernel regression estimator. This is proven by the small grade MSE which is given by this estimator. [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , NOVITA EKA CHANDRA and , Prof. Dr. Sri Haryatmi, M.Sc. (2014) REGRESI NONPARAMETRIK KERNEL ADJUSTED. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74649
institution Universitas Gadjah Mada
building UGM Library
country Indonesia
collection Repository Civitas UGM
topic ETD
spellingShingle ETD
, NOVITA EKA CHANDRA
, Prof. Dr. Sri Haryatmi, M.Sc.
REGRESI NONPARAMETRIK KERNEL ADJUSTED
description Nadaraya watson�s kernel adjusted regression estimator is an estimator that whose kernel is taken from the family of scale-location associated with the classical kernel density estimator. Based on these estimator, it can be obtained optimal bandwith and scale parameter. This estimator gives a better estimation results compared with Naradaya Watson�s clasical kernel regression estimator. This is proven by the small grade MSE which is given by this estimator.
format Theses and Dissertations
NonPeerReviewed
author , NOVITA EKA CHANDRA
, Prof. Dr. Sri Haryatmi, M.Sc.
author_facet , NOVITA EKA CHANDRA
, Prof. Dr. Sri Haryatmi, M.Sc.
author_sort , NOVITA EKA CHANDRA
title REGRESI NONPARAMETRIK KERNEL ADJUSTED
title_short REGRESI NONPARAMETRIK KERNEL ADJUSTED
title_full REGRESI NONPARAMETRIK KERNEL ADJUSTED
title_fullStr REGRESI NONPARAMETRIK KERNEL ADJUSTED
title_full_unstemmed REGRESI NONPARAMETRIK KERNEL ADJUSTED
title_sort regresi nonparametrik kernel adjusted
publisher [Yogyakarta] : Universitas Gadjah Mada
publishDate 2014
url https://repository.ugm.ac.id/133812/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74649
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