TY - JOUR

T1 - Semiparametric regression curve estimation for longitudinal data using mixed spline truncated and fourier series estimator

AU - Wening, A. W.

AU - Budiantara, I. N.

AU - Zain, I.

N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

PY - 2020/6/19

Y1 - 2020/6/19

N2 - Semiparametric regression is a regression approach that is used when the form of regression curve is assumed to be partly known and partly unknown. In the semiparametric regression model, the same type of estimation method is generally used for some or all of the predictor variables. There are many examples of data cases that have different patterns on each predictor variable, so if the data is forced to use only one form of estimator to estimate the regression curve, it will produce estimation that will not be appropriate to the data pattern. Therefore, a mixed estimator needs to be developed. However, mixed estimator in previous studies was only able to handle cross-sectional data. So this study uses longitudinal data that has advantages, one of which is estimation of each individual characteristic and time (period) characteristic are obtained separately in longitudinal data. Therefore, this study develops a mixed Spline Truncated and Fourier Series estimator in semiparametric regression for longitudinal data using the Weighted Least Square method. Estimation of the semiparametric regression model for longitudinal data using mixed estimator of Spline Truncated and Fourier Series is where D (K,m) = XA (K,m) + TB (K,m) + ZC (K,m). The selection of the best model is based on knot points and optimum oscillation parameters that should be selected optimally using minimum Generalized Cross Validation (GCV) value.

AB - Semiparametric regression is a regression approach that is used when the form of regression curve is assumed to be partly known and partly unknown. In the semiparametric regression model, the same type of estimation method is generally used for some or all of the predictor variables. There are many examples of data cases that have different patterns on each predictor variable, so if the data is forced to use only one form of estimator to estimate the regression curve, it will produce estimation that will not be appropriate to the data pattern. Therefore, a mixed estimator needs to be developed. However, mixed estimator in previous studies was only able to handle cross-sectional data. So this study uses longitudinal data that has advantages, one of which is estimation of each individual characteristic and time (period) characteristic are obtained separately in longitudinal data. Therefore, this study develops a mixed Spline Truncated and Fourier Series estimator in semiparametric regression for longitudinal data using the Weighted Least Square method. Estimation of the semiparametric regression model for longitudinal data using mixed estimator of Spline Truncated and Fourier Series is where D (K,m) = XA (K,m) + TB (K,m) + ZC (K,m). The selection of the best model is based on knot points and optimum oscillation parameters that should be selected optimally using minimum Generalized Cross Validation (GCV) value.

UR - http://www.scopus.com/inward/record.url?scp=85088314950&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1538/1/012061

DO - 10.1088/1742-6596/1538/1/012061

M3 - Conference article

AN - SCOPUS:85088314950

SN - 1742-6588

VL - 1538

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012061

T2 - 3rd International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2019

Y2 - 26 October 2019 through 27 October 2019

ER -