TY - JOUR
T1 - The performance of nonparametric regression for trend and seasonal pattern in longitudinal data
AU - Mardianto, M. Fariz Fadillah
AU - Kartiko, Sri Haryatmi
AU - Utami, Herni
N1 - Funding Information:
The authors appreciate the support of the Gadjah Mada University and Airlangga University, as well as Lembaga Pengelola Dana Pendidikan (LPDP) for the success of this study. We are also grateful to The Ministry of Finance in Indonesia for the doctoral scholarship given to the authors.
Publisher Copyright:
© 2006-2020 Asian Research Publishing Network (ARPN).
PY - 2020
Y1 - 2020
N2 - Recently, nonparametric regression does not only develop in cross section data but also in longitudinal data. Longitudinal data have repeated measurements in each subject. In the measurements for each subject sometimes there is a trend, seasonal, also combination between trend and seasonal data pattern. In this study, the performance of nonparametric regression estimators for longitudinal data related to model trend seasonal data pattern is compared by using Mean Square Error (MSE), Generalized Cross Validation (GCV) and determination coefficient value as goodness of indicator. The estimators that be used is truncated spline, Nadaraya Watson kernel, and Fourier series with include cosines and sines bases. This paper has contribution to introduce Fourier series, the new estimator for longitudinal data, as an alternative estimator for modeling trend and seasonal data. The result, the Fourier series estimator has the best performance indicators in modeling trend and seasonal data pattern for longitudinal data when compared with the estimator that be developed early in nonparametric regression for longitudinal data, such as spline and kernel. The result is important for data analysis in nonparametric regression for longitudinal data because there is data pattern with trend seasonal in many applications that need suitable estimator.
AB - Recently, nonparametric regression does not only develop in cross section data but also in longitudinal data. Longitudinal data have repeated measurements in each subject. In the measurements for each subject sometimes there is a trend, seasonal, also combination between trend and seasonal data pattern. In this study, the performance of nonparametric regression estimators for longitudinal data related to model trend seasonal data pattern is compared by using Mean Square Error (MSE), Generalized Cross Validation (GCV) and determination coefficient value as goodness of indicator. The estimators that be used is truncated spline, Nadaraya Watson kernel, and Fourier series with include cosines and sines bases. This paper has contribution to introduce Fourier series, the new estimator for longitudinal data, as an alternative estimator for modeling trend and seasonal data. The result, the Fourier series estimator has the best performance indicators in modeling trend and seasonal data pattern for longitudinal data when compared with the estimator that be developed early in nonparametric regression for longitudinal data, such as spline and kernel. The result is important for data analysis in nonparametric regression for longitudinal data because there is data pattern with trend seasonal in many applications that need suitable estimator.
KW - Longitudinal data
KW - Nonparametric regression
KW - Performance indicator
KW - Trend and seasonal data pattern
UR - http://www.scopus.com/inward/record.url?scp=85086506199&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85086506199
SN - 1819-6608
VL - 15
SP - 1111
EP - 1115
JO - ARPN Journal of Engineering and Applied Sciences
JF - ARPN Journal of Engineering and Applied Sciences
IS - 9
ER -