Amanote Research

Amanote Research

    RegisterSign In

Improving Credit Risk Analysis With Cluster Based Modeling and Threshold Selection

doi 10.24251/hicss.2020.174
Full Text
Open PDF
Abstract

Available in full text

Date

January 1, 2020

Authors
Ajay Byanjankar
Publisher

Hawaii International Conference on System Sciences


Related search

Modeling Credit Risk With Partial Information

SSRN Electronic Journal
2004English

Large Portfolio Credit Risk Modeling

International Journal of Theoretical and Applied Finance
EconomicsEconometricsFinance
2007English

Enterprise Credit Risk Evaluation Modeling and Empirical Analysis via GRNN Neural Network

International Journal of Economics and Finance
2015English

Credit Scoring Modeling With State-Dependent Sample Selection: A Comparison Study With the Usual Logistic Modeling

Pesquisa Operacional
Management ScienceOperations Research
2015English

Threshold Selection for 60 GHzTOA Estimation Based on Skewness and Kurtosis Analysis

International Journal of Smart Home
Computer Science
2016English

Heteroscedastic Discriminant Analysis Combined With Feature Selection for Credit Scoring

Statistics in Transition
UncertaintyStatisticsProbability
2016English

Managing Credit Risk With Credit and Macro Derivatives

Schmalenbach Business Review
2004English

Erratum To: Credit Risk Management: Pricing, Measurement, and Modeling

2017English

Cluster Analysis in Retail Segmentation for Credit Scoring

Croatian Operational Research Review
StatisticsProbabilityUncertaintyApplied MathematicsEconomicsManagement ScienceEconometricsOperations Research
2014English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy