THE PARAMETRIC AND NONPARAMETRIC ESTIMATOR IN SEMIPARAMETRIC REGRESSION FOR LONGITUDINAL DATA WITH SPLINE APPROACH

Authors

  • Tony Yulianto Universitas Islam Madura, Indonesia
  • Kuzairi Kuzairi Universitas Islam Madura, Indonesia http://orcid.org/0000-0002-4769-0227
  • Noer Azizah Universitas Islam Madura, Indonesia
  • M. Fariz Fadillah Mardianto Universitas Airlangga, Indonesia
  • Ira Yuditira Universitas Islam Madura, Indonesia
  • Faisol Faisol Universitas Islam Madura, Indonesia http://orcid.org/0000-0003-2900-1448
  • Rica Amalia Universitas Islam Madura, Indonesia http://orcid.org/0000-0002-2586-7124

DOI:

https://doi.org/10.21107/kursor.v11i4.316

Keywords:

Semiparametric Regression, Longitudinal Data, Spline, GCV (Generalized Cross Validation), Electricity Consumption in Madura

Abstract

Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression. In this study, the form of spline semiparametric regression curve estimator for longitudinal data assessed. Based on the estimator that be obtained by using Weighted Least Square (WLS) optimization applied to model electricity consumption in Madura by choosing a model for longitudinal data based on linear spline estimator with two knot. The good criterion of the model is using the GCV value, the coefficient of determination and the value of MSE. The best model is a model that has a high coefficient of determination and a small MSE value. This spline model has a determination coefficient value of 99,72911% and MSE 32,50458.

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References

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Published

2023-01-06

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