Pages that link to "Item:Q1578940"
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The following pages link to Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications (Q1578940):
Displaying 17 items.
- An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market (Q320966) (← links)
- Choosing the best set of variables in regression analysis using integer programming (Q839047) (← links)
- Solving a large scale semi-definite logit model (Q970129) (← links)
- Multi-step methods for choosing the best set of variables in regression analysis (Q989838) (← links)
- Analyzing credit risk data: a comparison of logistic discrimination, classification tree analysis, and feedforward networks (Q1966033) (← links)
- Predicting mortgage early delinquency with machine learning methods (Q2029349) (← links)
- Credit scoring based on the set-valued identification method (Q2220404) (← links)
- Classification of companies using maximal margin ellipsoidal surfaces (Q2376124) (← links)
- A two step algorithm for solving a large scale semi-definite logit model (Q2468775) (← links)
- Establishing decision tree-based short-term default credit risk assessment models (Q2834635) (← links)
- Risk prediction with machine learning and regression methods (Q2875754) (← links)
- Assessing naïve Bayes as a method for screening credit applicants (Q3184467) (← links)
- Design of adaptive Elman networks for credit risk assessment (Q4991078) (← links)
- Application of Discriminant Analysis, Factor Analysis, Logistic Regression, and KMV-Merton Model in Credit Risk Analysis (Q5139575) (← links)
- Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study (Q5858901) (← links)
- Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method (Q6103193) (← links)
- Credit risk analysis using boosting methods (Q6115491) (← links)
