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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/3909401 |
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| _version_ | 1832556696651694080 |
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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 | 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 |
| id | doaj-art-a214cdf71fb647abb9123eaa5607bc73 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-a214cdf71fb647abb9123eaa5607bc732025-02-03T05:44:38ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/3909401A New Mixed Estimator in Nonparametric Regression for Longitudinal DataMade Ayu Dwi Octavanny0I Nyoman Budiantara1Heri Kuswanto2Dyah Putri Rahmawati3Department of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsWe 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.http://dx.doi.org/10.1155/2021/3909401 |
| spellingShingle | Made Ayu Dwi Octavanny I Nyoman Budiantara Heri Kuswanto Dyah Putri Rahmawati A New Mixed Estimator in Nonparametric Regression for Longitudinal Data Journal of Mathematics |
| title | 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_short | A New Mixed Estimator in Nonparametric Regression for Longitudinal Data |
| title_sort | new mixed estimator in nonparametric regression for longitudinal data |
| url | http://dx.doi.org/10.1155/2021/3909401 |
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