Application of Logical Regression Function Model in Credit Business of Commercial Banks

[1] [1] J Lin, Han L. Lattice clustering and its application in credit risk management of commercial banks[J]. Procedia Computer Science, 2021, 183:145-151. Lin J Han L Lattice clustering and its application in credit risk management of commercial banks[J] Procedia Computer Science 2021 183 145 151 Search in Google Scholar

[2] [2] Zhong, Zhi, Carr, et al. Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 C Reservoir oil minimum miscibility pressure prediction.[J]. Fuel, 2016, 184:590-603. Zhong Zhi Carr Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 C Reservoir oil minimum miscibility pressure prediction.[J] Fuel 2016 184 590 603 Search in Google Scholar

[3] [3] Ho A, Citrin J, F Auriemma, et al. Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling[J]. Nuclear fusion, 2019, 59(5):056007.1-056007.18. Ho A Citrin J Auriemma F Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling[J] Nuclear fusion 2019 59 5 056007.1 056007.18 Search in Google Scholar

[4] [4] Murari A, Peluso E, Lungaroni M, et al. Application of symbolic regression to the derivation of scaling laws for tokamak energy confinement time in terms of dimensionless quantities[J]. Nuclear Fusion, 2016, 56(2):026005. Murari A Peluso E Lungaroni M Application of symbolic regression to the derivation of scaling laws for tokamak energy confinement time in terms of dimensionless quantities[J] Nuclear Fusion 2016 56 2 026005 Search in Google Scholar

[5] [5] Najafzadeh M, Laucelli D B, Zahiri A. Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes[J]. KSCE Journal of Civil Engineering, 2017, 21(5):1956-1963. Najafzadeh M Laucelli D B Zahiri A Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes[J] KSCE Journal of Civil Engineering 2017 21 5 1956 1963 Search in Google Scholar

[6] [6] Rim H, Park S, Oh C, et al. Application of locally weighted regression-based approach in correcting erroneous individual vehicle speed data[J]. Journal of Advanced Transportation, 2016, 50(2):180-196. Rim H Park S Oh C Application of locally weighted regression-based approach in correcting erroneous individual vehicle speed data[J] Journal of Advanced Transportation 2016 50 2 180 196 Search in Google Scholar

[7] [7] Chu H, Wei J, Li T, et al. Application of Support Vector Regression for Mid- and Long-term Runoff Forecasting in “Yellow River Headwater” Region[J]. Procedia Engineering, 2016, 154:1251-1257. Chu H Wei J Li T Application of Support Vector Regression for Mid- and Long-term Runoff Forecasting in “Yellow River Headwater” Region[J] Procedia Engineering 2016 154 1251 1257 Search in Google Scholar

[8] [8] Seeboonruang U. An application of time-lag regression technique for assessment of groundwater fluctuations in a regulated river basin: a case study in Northeastern Thailand[J]. Environmental Earth Sciences, 2015, 73(10):6511-6523. Seeboonruang U An application of time-lag regression technique for assessment of groundwater fluctuations in a regulated river basin: a case study in Northeastern Thailand[J] Environmental Earth Sciences 2015 73 10 6511 6523 Search in Google Scholar

[9] [9] Arslan D. The Comparison Study of Hybrid Method with RDTM for Solving Rosenau-Hyman Equation[J]. Applied Mathematics and Nonlinear Sciences, 2020, 5(1):267-274. Arslan D The Comparison Study of Hybrid Method with RDTM for Solving Rosenau-Hyman Equation[J] Applied Mathematics and Nonlinear Sciences 2020 5 1 267 274 Search in Google Scholar

[10] [10] Aidara S, Sagna Y. BSDEs driven by two mutually independent fractional Brownian motions with stochastic Lipschitz coefficients[J]. Applied Mathematics and Nonlinear Sciences, 2019, 4(1):151-162. Aidara S Sagna Y BSDEs driven by two mutually independent fractional Brownian motions with stochastic Lipschitz coefficients[J] Applied Mathematics and Nonlinear Sciences 2019 4 1 151 162 Search in Google Scholar