Estimation of Regression Function in Multi-Response Nonparametric Regression Model Using Smoothing Spline and Kernel Estimators

B. Lestari, Fatmawati, I. N. Budiantara, N. Chamidah

Research output: Contribution to journalConference articlepeer-review

38 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number012091
JournalJournal of Physics: Conference Series
Volume1097
Issue number1
DOIs
Publication statusPublished - 12 Oct 2018
Event5th International Conference on Research, Implementation, and Education of Mathematics and Science, ICRIEMS 2018 - Yogyakarta, Indonesia
Duration: 7 May 20188 May 2018

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