ANOVA Decomposition and Importance Variable Process in Multivariate Adaptive Regression Spline Model
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G. K. Kate, C. B. Nayak and S. B. Thakare, "Optimization of sustainable high-strength-high-volume fly ash concrete with and without steel fiber using Taguchi method and multi-regression analysis," Innovative Infrastructure Solutions, vol. 6, no. 2, pp. 1-18, 2021.
T. B. Armstrong and M. Kolesar, "Simple and honest confidence intervals in non-parametric regression," Quantitative Economics, vol. 11, no. 1, pp. 1-39, 2020.
D. P. Rahmawati, I. N. Budiantara, D. D. Prastyo and M. A. D. Octavanny, "Mixed Spline Smoothing and Kernel Estimator in Biresponse Non-parametric Regression," International Journal of Mathematics and Mathematical Sciences, 2021.
M. A. Sahraei, H. Duman, M. Y. Codur and E. Eyduran, "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, p. 224, 2021.
W. Zhang, C. Wu, Y. Li, L. Wang and P. Samui, "Assessment of pile drivability using random forest regression and multivariate adaptive regression splines," Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 15, no. 1, pp. 27-40, 2021.
C. K. Arthur, V. A. Temeng and Y. Y. Ziggah, "Multivariate Adaptive Regression Splines (MARS) approach to blast-induced ground vibration prediction," International Journal of Mining, Reclamation and Environment , vol. 34, no. 3, pp. 198-222, 2020.
D. H. W. Li, W. Chen, S. Li and S. Lou, "Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) - A case study of Hong Kong," Energy, vol. 186, 2019.
N. B. Serrano, A. S. Sanchez, f. S. Lasheras, F. J. Iglesias-Rodriguez and G. F. Valverde, "Identification of gender differences in the factors influencing shoulders, neck and upper limb MSD by means of multivariate adaptive regression splines (MARS)," Applied Ergonomics, vol. 82, 2020.
K. Sun, M. Rajabtabar, S. Samadi, M. Rezaie-Balf, A. Ghaemi, S. S. Band and A. Mosavi, "An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction," Engineering Applications of Computational Fluid Mechanics, vol. 15, no. 1, pp. 251-271, 2021.
Y. Liu, W. Tian and X. Zhou, "Energy and carbon performance of urban buildings using metamodeling variable importance techniques," Building Simulation, vol. 14, pp. 535-547, 2021.
M. Kumar and P. Samui, "Reliability Analysis of Pile Foundation Using ELM and MARS," Geotechnical and Geological Engineering, vol. 37, pp. 3447-3457, 2019.
A. Maleki, M. Elahi, M. E. H. Assad, M. A. Nazari, M. S. Shadloo and N. Nabipour, "Thermal conductivity modeling of nanofluids with ZnO particles by using approaches based on artificial neural network and MARS," Journal of Thermal Analysis and Calorimetry, vol. 143, pp. 4261-4272, 2021.
Z. A. Al-Sudani, S. Q. Salih, A. Sharafati and Z. M. Yaseen, "Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation," Journal of Hydrology, vol. 573, pp. 1-12, 2019.
S. C. Gupta and V. K. Kapoor, Fundamentals of mathematical statistics, New-Delhi: Sultan Chand & Sons, 2020.
A. E. Marques, P. A. Prates, A. F. G. Pereira, M. C. Oliveira, J. V. Fernandes and B. M. Ribeiro, "Performance Comparison of Parametric and Non-Parametric Regression Models for Uncertainty Analysis of Sheet Metal Forming Processes," Metals, vol. 10, no. 4, p. 457, 2020.
D. J. Henderson and A.-C. Souto, "An Introduction to Non-parametric Regression for Labor Economists," Journal of Labor Research, vol. 39, pp. 355-382, 2018.
D. P. Rahmawati, I. N. Budiantara, D. D. Prastyo and M. A. D. Octavanny, "Modeling of Human Development Index in Papua Province Using Spline Smoothing Estimator in Non-parametric Regression," in 3rd International Conference on Statistics, Mathematics, Teaching, and Research , Makassar, 2021.
M. Kobayashi, K. Hoshina, Y. Nemoto, S. Takagi, M. Shojima, M. Hayakawa, S. Yamada and M. Oshima, "A penalized spline fitting method to optimize geometric parameters of arterial centerlines extracted from medical images," Computerized Medical Imaging and Graphics, vol. 84, 2020.
N. P. A. M. Mariati, I. N. Budiantara and V. Ratnasari, "The Application of Mixed Smoothing Spline and Fourier Series Model in Non-parametric Regression," Symmetry, vol. 13, no. 11, 2021.
R. Hidayat, I. N. Budiantara, B. W. Otok and V. Ratnasari, "The regression curve estimation by using mixed smoothing spline and kernel (MsS-K) model," Communications in Statistics - Theory and Methods, vol. 50, no. 17, pp. 3942-3953, 2019.
R. Hidayat, I. N. Budiantara, B. W. Otok and V. Ratnasari, "An Extended Model of Penalized Spline with The Addition of Kernel Functions in Non-parametric Regression Model," Applied Mathematics & Information Sciences, vol. 13, no. 3, pp. 453-460, 2019.
M. A. D. Octavanny, I. N. Budiantara, H. Kuswanti and D. P. Rahmawati, "Non-parametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series," Abstract and Applied Analysis, 2020.
D. A. Widyastuti, A. A. R. Fernandes and H. Pramoedyo, "Spline estimation method in non-parametric regression using truncated spline approach," in 1 st International Conference on Mathematics and its Applications (ICoMathApp) , Malang, 2020.
S. D. P. Yasmirullah, B. W. Otok, J. D. T. Purnomo and D. D. Prastyo, "Modification of Multivariate Adaptive Regression Spline (MARS)," in International Conference on Mathematics, Statistics and Data Science (ICMSDS), Bogor, 2020.
D. D. Prawanti, I. N. Budiantara and J. D. T. Purnomo, "Parameter Interval Estimation of Semiparametric Spline Truncated Regression Model for Longitudinal Data," IOP Conference Series: Materials Science and Engineering, vol. 546, no. 5, 2019.
M. Ramli, V. Ratnasari and I. N. Budiantara, "Estimation of Matrix Variance-Covariance on Non-parametric Regression Spline Truncated for Longitudinal Data," in The 15th International Symposium on Geometric Function Theory and Applications , Malang, 2020.
M. P. Wand, "A Comparison of Regression Spline Smoothing Procedures," Computational Statistics, vol. 15, pp. 443-462, 2000.
A. Ghaemi, M. Rezaie-Balf, J. Adamowski, O. Kisi and J. Quilty, "On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction," Agricultural and Forest Meteorology, vol. 278, 2019.
S. K. Depren and M. T. Kartal, "Prediction on the volume of non-performing loans in Turkey using multivariate adaptive regression splines approach," International Journal of Finance & Economics, vol. 26, no. 4, pp. 6395-6405, 2021.
B. W. Otok, R. Y. Putra, Sutikno and S. D. P. Yasmirullah, "Bootstrap Aggregating Multivariate Adaptive Regression Spline for Observational Studies in Diabetes Cases," Systematic Reviews in Pharmacy, vol. 11, no. 8, pp. 406-413, 2020.
N. Santoso and S. P. Wulandari, "Hybrid Support Vector Machine to Preterm Birth Prediction," Indonesian Journal of Electronics and Instrumentation Systems (IJEIS), vol. 8, no. 2, pp. 191-200, 2018.
P. J. García-Nieto, E. García-Gonzalo, J. R. A. Ferní¡ndez and C. D. Muñiz, "Modeling eutrophication risks in Tanes reservoir by using a hybrid WOA optimized SVR-relied technique along with feature selection based on the MARS approximation," Stochastic Environmental Research and Risk Assessment, 2021.
J. H. Friedman, "Multivariate Adaptive Regression Splines," Ann. Statist, vol. 19, no. 1, pp. 1-67, 1991.
M. H. Ahmadi, B. Mohseni-Gharyehsafa, M. Farzaneh-Gord, R. D. Jilte, R. Kumar and K.-w. Chau, "Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms," Engineering Applications of Computational Fluid Mechanics, vol. 13, no. 1, pp. 220-228, 2019.
S. D. P. Yasmirullah, B. W. Otok, J. D. T. Purnomo and D. D. Prastyo, "Multivariate adaptive regression spline (MARS) methods with application to multi drug-resistant tuberculosis (MDR-TB) prevalence," in AIP Conference Proceedings, 2021.
J. Diaz, F. J. Fernandez and M. M. Prieto, "Hot Metal Temperature Forecasting at Steel Plant Using Multivariate Adaptive Regression Splines," Metals, vol. 10, no. 1, p. 41, 2020.
G. E. B. Archer, A. Saltelli and I. M. Sobol, "Sensitivity measures,anova-like Techniques and the use of bootstrap," Journal of Statistical Computation and Simulation, vol. 58, no. 2, pp. 99-120, 1997.
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