Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression

2017 ◽  
Vol 70 ◽  
pp. 148-154 ◽  
Author(s):  
Anthony T.C. Goh ◽  
Yanmei Zhang ◽  
Runhong Zhang ◽  
Wengang Zhang ◽  
Yang Xiao
2020 ◽  
Vol 4 (1) ◽  
pp. 39
Author(s):  
Ananto Wibowo ◽  
M. Rismawan Ridha

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.


Energy ◽  
2021 ◽  
Vol 224 ◽  
pp. 120090
Author(s):  
Mohammad Ali Sahraei ◽  
Hakan Duman ◽  
Muhammed Yasin Çodur ◽  
Ecevit Eyduran

Sign in / Sign up

Export Citation Format

Share Document