TY - JOUR
T1 - Estimation of Regression Function in Multi-Response Nonparametric Regression Model Using Smoothing Spline and Kernel Estimators
AU - Lestari, B.
AU - Fatmawati,
AU - Budiantara, I. N.
AU - Chamidah, N.
N1 - Publisher Copyright:
© 2018 Published under licence by IOP Publishing Ltd.
PY - 2018/10/12
Y1 - 2018/10/12
N2 - The functions which describe relationship of more than one response variables observed at several values of the predictor variables in which there are correlations among the responses can be estimated by using a multi-response nonparametric regression model approach. In this study, we discuss about how we estimate the regression function of the multi-response nonparametric regression model by using both smoothing spline and kernel estimators. The principal objective is determining the smoothing spline and kernel estimators to estimate the regression function of the multi-response nonparametric regression model. The obtained results show that the regression functions obtained by using smoothing spline and kernel estimators are mathematically just distinguished by their smoother matrices. In addition, they are linear in observation and bias estimators.
AB - The functions which describe relationship of more than one response variables observed at several values of the predictor variables in which there are correlations among the responses can be estimated by using a multi-response nonparametric regression model approach. In this study, we discuss about how we estimate the regression function of the multi-response nonparametric regression model by using both smoothing spline and kernel estimators. The principal objective is determining the smoothing spline and kernel estimators to estimate the regression function of the multi-response nonparametric regression model. The obtained results show that the regression functions obtained by using smoothing spline and kernel estimators are mathematically just distinguished by their smoother matrices. In addition, they are linear in observation and bias estimators.
UR - http://www.scopus.com/inward/record.url?scp=85055334005&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1097/1/012091
DO - 10.1088/1742-6596/1097/1/012091
M3 - Conference article
AN - SCOPUS:85055334005
SN - 1742-6588
VL - 1097
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012091
T2 - 5th International Conference on Research, Implementation, and Education of Mathematics and Science, ICRIEMS 2018
Y2 - 7 May 2018 through 8 May 2018
ER -