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Pengaruh Indeks Massa Tubuh dan TrigliseridaTerhadap Gula Darah dengan Model Regresi Nonparametrik Spline Biprediktor

Authors

  • Dewi Rahma Ente Universitas Hasanuddin
  • Anna Islamiyati Universitas Hasanuddin
  • Raupong Raupong Universitas Hasanuddin

Keywords:

Nonparametric regression, Spline truncated, Diabetes Mellitus, GCV

Abstract

The regression approach can be carried out using three approaches namely parametric, nonparametric and semiparametric approaches. Nonparametric regression is a statistical method used to see the relationship between the response variable and the predictor variable when the shape of the data curve is unknown. Diabetes mellitus (DM) or commonly called diabetes is a disease that is found and observed in various parts of the world today. DM is often marked by a significant increase in blood sugar levels. In this study using blood sugar levels as response variables, body mass index and triglycerides as predictor variables. Data were analyzed using truncated linear spline with one, two and three point knots experiments. The best model is obtained based on the minimum generalized cross validation (GCV) value. The results obtained that the best model is linear spline using three point knots.

References

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Eubank, R.L. Spline Smoothing and Nonparametric Regression, Marcel Deker, New York. 1988.

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Islamiyati, A., Fatmawati, and Chamidah, N. Penalized Spline Estimator with Multi Smoothing Parameters in Bi-Response Multi-Predictor Nonparametric Regression Model For Longitudinal Data . Songklanakarin Journal of Sciences and Technology, 42(4): 897-909, 2020.

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Aprilia, B., Islamiyati, A., Anisa, dan Ilyas, N. Estimasi Model Regresi Kuantil Spline Kuadratik pada Data Trombosit dan Hematokrit Pasien DBD. ESTIMASI: Journal of Statistics and Its Application, 1 (2) : 58-64, 2020.

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Published

2021-07-20 — Updated on 2021-07-22

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Vol. 2, No. 2, Juli, 2021 : Estimasi

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