A New Mixed Estimator in Nonparametric Regression for Longitudinal Data

We introduce a new method for estimating the nonparametric regression curve for longitudinal data. This method combines two estimators: truncated spline and Fourier series. This estimation is completed by minimizing the penalized weighted least squares and weighted least squares. This paper also pro...

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Autores principales:	Made Ayu Dwi Octavanny, I Nyoman Budiantara, Heri Kuswanto, Dyah Putri Rahmawati
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a214cdf71fb647abb9123eaa5607bc73
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spelling	oai:doaj.org-article:a214cdf71fb647abb9123eaa5607bc732021-11-29T00:56:52ZA New Mixed Estimator in Nonparametric Regression for Longitudinal Data2314-478510.1155/2021/3909401https://doaj.org/article/a214cdf71fb647abb9123eaa5607bc732021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3909401https://doaj.org/toc/2314-4785We introduce a new method for estimating the nonparametric regression curve for longitudinal data. This method combines two estimators: truncated spline and Fourier series. This estimation is completed by minimizing the penalized weighted least squares and weighted least squares. This paper also provides the properties of the new mixed estimator, which are biased and linear in the observations. The best model is selected using the smallest value of generalized cross-validation. The performance of the new method is demonstrated by a simulation study with a variety of time points. Then, the proposed approach is applied to a stroke patient dataset. The results show that simulated data and real data yield consistent findings.Made Ayu Dwi OctavannyI Nyoman BudiantaraHeri KuswantoDyah Putri RahmawatiHindawi LimitedarticleMathematicsQA1-939ENJournal of Mathematics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Made Ayu Dwi Octavanny I Nyoman Budiantara Heri Kuswanto Dyah Putri Rahmawati A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
description	We introduce a new method for estimating the nonparametric regression curve for longitudinal data. This method combines two estimators: truncated spline and Fourier series. This estimation is completed by minimizing the penalized weighted least squares and weighted least squares. This paper also provides the properties of the new mixed estimator, which are biased and linear in the observations. The best model is selected using the smallest value of generalized cross-validation. The performance of the new method is demonstrated by a simulation study with a variety of time points. Then, the proposed approach is applied to a stroke patient dataset. The results show that simulated data and real data yield consistent findings.
format	article
author	Made Ayu Dwi Octavanny I Nyoman Budiantara Heri Kuswanto Dyah Putri Rahmawati
author_facet	Made Ayu Dwi Octavanny I Nyoman Budiantara Heri Kuswanto Dyah Putri Rahmawati
author_sort	Made Ayu Dwi Octavanny
title	A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
title_short	A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
title_full	A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
title_fullStr	A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
title_full_unstemmed	A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
title_sort	new mixed estimator in nonparametric regression for longitudinal data
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a214cdf71fb647abb9123eaa5607bc73
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A New Mixed Estimator in Nonparametric Regression for Longitudinal Data

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