Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama

Authors

  • Samsul Arifin Hasanuddin University
  • Anna Islamiyati Hasanuddin University
  • Raupong Raupong Hasanuddin University

DOI:

https://doi.org/10.20956/ejsa.v1i1.9262

Keywords:

Principal Component Analysis, Diabetes Data, Linear, Multicollinearity, Spline.

Abstract

In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.

Author Biographies

Samsul Arifin, Hasanuddin University

Department of Statistics

Anna Islamiyati, Hasanuddin University

Department of Statistics

Raupong Raupong, Hasanuddin University

Department of Statistics

References

Islamiyati, A. Spline Polynomial Truncated dalam Regresi Nonparametrik. Jurnal Matematika, Statistika & Komputasi, 14 (1) : 54-60, 2017.

Daoud, J.I. Multicollinearity and Regression Analysis. J. Phys. Conf. Ser. 949 01200, 2017.

Johnson, R.A. & Wichern, D. W. 2002. Applied Multivariate Statistical Analysis. Pentice Hall Inc, New Jersey.

Islamiyati, A. Estimasi Parameter Model Regresi Logistik Biner Komponen Utama Non Linear dengan Maksimum Likelihood. Jurnal Matematika, Statistika dan Komputasi, 11 (2) : 122-128, 2015.

Melany, M. Penerapan Robust Principal Component Analysis untuk data yang mengandung Outlier. Skripsi. Universitas Hasanuddin, Makassar. 2017.

Tharwat, A. Principal Component Analysis-a Tutorial. ResearcGate. 2017.

Hoffman, H., Schaal, S. & Vijayakumar, S. Local Dimensionality Reduction for Non-Parametric Regression. Neural Process Lett, 29 : 109-131, 2009.

Li, B.Y & Hsing, T. Uniform Convergence Rates For Nonparametric Regression and Principal Component Analysis in Longitudinal Data. The Annals of Statistics, 38 (6) : 3321-3351, 2010.

Islamiyati, A. Regresi Spline Polynomial Truncated Biprediktor untuk Identifikasi Perubahan Jumlah Trombosit Pasien Demam Berdarah Dengue. Al khwarizmi, 7 (2) : 97-110, 2019.

Islamiyati, A., Fatmawati & Chamidah, N. Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression. Journal of Physics: Conf. Series. 979 pp 012093, 2018.

Islamiyati, A., Fatmawati & Chamidah, I.N. Penalized Spline Estimator with Multi Smoothing Parameters in Biresponse Multipredictor Regression Model for Longitudinal Data. Songklanakarin Journal of Science and Technology, In Press SJST-2018-0423.R2, 2019.

Islamiyati, A., Fatmawati & Chamidah, I.N. Changes in Blood Glucosa 2 Hours After Meals in Type 2 Diabetes Patients based on Length of Treatment at Hasanuddin University Hospital, Indonesia. Rawal Medical Journal, 45 (1) : 31-34, 2020.

Islamiyati, A., Raupong & Anisa. Use of Penalized Spline Linear to Identify Change in Pattern of Blood Sugar based on the Weight of Diabetes Patients. Int. J. Acad. Appl. Res., 3 Issue 12 : 75-78, 2019.

Downloads

Published

2020-01-31