Quantification of model risk that is caused by model misspecification

DOI10.1080/02664763.2020.1849055OpenAlexW3105324452MaRDI QIDQ5073380

H. P. Mashele, M. B. Seitshiro

Publication date: 6 May 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2020.1849055

zbMATH Keywords

model misspecificationbinary logistic regressionprobability of defaultcomplementary log-log

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• QRM
• SMOTE

Cites Work

• Assessing financial model risk
• Zero‐Altered and other Regression Models for Count Data with Added Zeros
• Complementary Log–Log Regression for the Estimation of Covariate‐Adjusted Prevalence Ratios in the Analysis of Data from Cross‐Sectional Studies
• Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model
• Bankruptcy prediction by generalized additive models
• The elements of statistical learning. Data mining, inference, and prediction
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