Comparison of Logistic Regression Model and MARS Using Multicollinearity Data Simulation

Authors

  • Ananto Wibowo Badan Pusat Statistik Kabupaten Cianjur
  • M. Rismawan Ridha Badan Pusat Statistik Kabupaten Maluku Tengah

DOI:

https://doi.org/10.31764/jtam.v4i1.1801

Keywords:

Logistic Regression, Multivariate Adaptive Regression Splines, Principal Component Analysis.

Abstract

There are several statistical methods used to model the effect of predictor variables on categorical response variables, namely logistic regression and Multivariate Adaptive Regression Splines (MARS). However, neither MARS nor logistic regression allows multicollinearity on any predictor variables. This study applies the use of both methods to the simulation data with principal component analysis as an improvement in multicollinearity to find out which regression has better performance. The result of the analysis shows that MARS is very powerful in modeling research simulation data. Besides, based on the criteria of the number of significant major components, accuracy, sensitivity, and specificity values, MARS has more appropriate performance than logistic regression.

References

Adityawardhani et al. (2017). Comparison of Logistic Regression Model and Mars Classification Results on Binary Response for Teknisi Ahli BBPLK Serang Training Graduates Status. DOARJ: International Journal of Humanities, Religion and Social Science, 2(1), 14-20

Diop et al. (2011). Maximum Likelihood Estimation in the Logistic Regression Model in a Cure Fraction. Electronic Journal of Statistics, 5(1), 460-483

Felicísimo et al. (2013). Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study. Landslides. 10(1), 175-189

Gwelo, Abubakari S. (2019). Principal Components To Overcome Multicollinearity Problem. Oradea Journal of Business and Economics, 4(1), 79-91

Hosmer, D., Lemeshow, S & Sturdivant X, R. (2013). Applied logistic regression Third Edition. New York: John Wiley & Sons,Inc.

Johnson RA, Wichern DW (2013). Applied Multivariate Statistical Analysis. Pearson New International Sixth Edition Paperback.

Kayri, M. (2010). The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines. Iranian Journal of Public Health, 39(2), 51-63

Kilinç et al. (2017). Using Multivariate Adaptive Regression Splines to Estimate Pollution in Soil. International Journal of Advanced and Applied Sciences, 4(2), 10-16

Koc, E. K. & Bozdogan, H. (2014). Model selection in multivariate adaptive regression splines (MARS) using information complexity as the ï¬tness function. Springer, June 2014, 35-58

Midi et al. (2013). Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics, Mei 2013, 253-267.

Mina, C. D., & Barrios, E. B. (2010). Profiling poverty with multivariate adaptive regression splines. Phillipine Journal of Development, 37(2): 55-97

Mukaka, M.M. (2012). Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71

Osetljivosti et al. (2014). Understanding sensitivity, specificity and predictive values. Vojnosanitetski pregled. Military-medical and Pharmaceutical Review, 71(11), 1062-1065.

Park et al. (2017). Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GI. Sustainability, 9(7), 1-20

Rahayu et al. (2017). Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014. International Journal of Educational Methodology, 3(2), 75-83.

Schober et al. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia and Analgesia, 126 (5), 1763-1768

Wahyuningrum, S. (2009). Mars Classification For Indigent Village/Sub-District at East Kalimantan in 2005. Surabaya: Institut Teknologi Sepuluh November.

Zewude et al. (2016). Binary Logistic Regression Analysis in Assessment and Identifying Factors That Influence Students’ Academic Achievement: The Case of College of Natural and Computational Science, Wolaita Sodo University, Ethiopia. Journal of Education and Practice, 7(25), 3-7.

Zhang, W & Goh, A. T. C. (2016). Multivariate adaptive regression splines and neural network model for prediction of pile drivability. Geoscience Frontiers, 7(1), 45-52.

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Published

2020-04-24

Issue

Vol. 4 No. 1 (2020): April

Section

Articles

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