Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series
Existing literature in nonparametric regression has established a model that only applies one estimator to all predictors. This study is aimed at developing a mixed truncated spline and Fourier series model in nonparametric regression for longitudinal data. The mixed estimator is obtained by solving...
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Format: | Article |
Language: | English |
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Wiley
2020-01-01
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Series: | Abstract and Applied Analysis |
Online Access: | http://dx.doi.org/10.1155/2020/4710745 |
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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 |
collection | DOAJ |
description | Existing literature in nonparametric regression has established a model that only applies one estimator to all predictors. This study is aimed at developing a mixed truncated spline and Fourier series model in nonparametric regression for longitudinal data. The mixed estimator is obtained by solving the two-stage estimation, consisting of a penalized weighted least square (PWLS) and weighted least square (WLS) optimization. To demonstrate the performance of the proposed method, simulation and real data are provided. The results of the simulated data and case study show a consistent finding. |
format | Article |
id | doaj-art-c5692fb4438147a0b74cbef19b88f978 |
institution | Kabale University |
issn | 1085-3375 1687-0409 |
language | English |
publishDate | 2020-01-01 |
publisher | Wiley |
record_format | Article |
series | Abstract and Applied Analysis |
spelling | doaj-art-c5692fb4438147a0b74cbef19b88f9782025-02-03T01:03:57ZengWileyAbstract and Applied Analysis1085-33751687-04092020-01-01202010.1155/2020/47107454710745Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier SeriesMade Ayu Dwi Octavanny0I. Nyoman Budiantara1Heri Kuswanto2Dyah Putri Rahmawati3Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaExisting literature in nonparametric regression has established a model that only applies one estimator to all predictors. This study is aimed at developing a mixed truncated spline and Fourier series model in nonparametric regression for longitudinal data. The mixed estimator is obtained by solving the two-stage estimation, consisting of a penalized weighted least square (PWLS) and weighted least square (WLS) optimization. To demonstrate the performance of the proposed method, simulation and real data are provided. The results of the simulated data and case study show a consistent finding.http://dx.doi.org/10.1155/2020/4710745 |
spellingShingle | Made Ayu Dwi Octavanny I. Nyoman Budiantara Heri Kuswanto Dyah Putri Rahmawati Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series Abstract and Applied Analysis |
title | Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series |
title_full | Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series |
title_fullStr | Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series |
title_full_unstemmed | Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series |
title_short | Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series |
title_sort | nonparametric regression model for longitudinal data with mixed truncated spline and fourier series |
url | http://dx.doi.org/10.1155/2020/4710745 |
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