TY - GEN
T1 - Nonparametric regression curve estimation using mixed spline truncated and kernel estimator for longitudinal data
AU - Maulidia, Miftahul Jannah
AU - Budiantara, I. Nyoman
AU - Purnomo, Jerry D.T.
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/18
Y1 - 2019/12/18
N2 - Nonparametric regression is a regression model approach that can be used if the pattern of the relationship between response and predictor variable is unknown. One of the most widely used nonparametric regression models is spline truncated, which has very good ability in handling data whose behavior changes at sub-specified intervals. While the kernel estimator is used to model data that does not have a specific pattern. Nonparametric regression models used by researchers have tended to assume that the pattern of the relationship between each predictor and response variable has the same pattern so that only one form of estimator is used, but in some applications the pattern of relationship between each predictor and response variable can be different from one another. In this condition are advised to use a mixed estimator for nonparametric regression curve estimation. However, the existing mixed estimator is only limited to cross section data. Theoretically mixed spline truncated and kernel estimators can be generalized to longitudinal data, which is repeated observation data in an experimental unit. The purpose of this study is to obtain nonparametric regression curve estimation using mixed spline truncated and kernel estimator for longitudinal data, it is done by completing the Weighted Least Square (WLS) optimization. The selection of the best model based on optimal knots and bandwidth for mixed spline truncated and kernel estimators was obtained using minimum Generalized Cross Validation (GCV) values.
AB - Nonparametric regression is a regression model approach that can be used if the pattern of the relationship between response and predictor variable is unknown. One of the most widely used nonparametric regression models is spline truncated, which has very good ability in handling data whose behavior changes at sub-specified intervals. While the kernel estimator is used to model data that does not have a specific pattern. Nonparametric regression models used by researchers have tended to assume that the pattern of the relationship between each predictor and response variable has the same pattern so that only one form of estimator is used, but in some applications the pattern of relationship between each predictor and response variable can be different from one another. In this condition are advised to use a mixed estimator for nonparametric regression curve estimation. However, the existing mixed estimator is only limited to cross section data. Theoretically mixed spline truncated and kernel estimators can be generalized to longitudinal data, which is repeated observation data in an experimental unit. The purpose of this study is to obtain nonparametric regression curve estimation using mixed spline truncated and kernel estimator for longitudinal data, it is done by completing the Weighted Least Square (WLS) optimization. The selection of the best model based on optimal knots and bandwidth for mixed spline truncated and kernel estimators was obtained using minimum Generalized Cross Validation (GCV) values.
UR - http://www.scopus.com/inward/record.url?scp=85077688683&partnerID=8YFLogxK
U2 - 10.1063/1.5139795
DO - 10.1063/1.5139795
M3 - Conference contribution
AN - SCOPUS:85077688683
T3 - AIP Conference Proceedings
BT - 2nd International Conference on Science, Mathematics, Environment, and Education
A2 - Indriyanti, Nurma Yunita
A2 - Ramli, Murni
A2 - Nurhasanah, Farida
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019
Y2 - 26 July 2019 through 28 July 2019
ER -