Mixture additive hazards cure model with latent variables: application to corporate default data
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Publication:2072400
DOI10.1016/j.csda.2021.107365OpenAlexW3206398959MaRDI QIDQ2072400
Qi Yang, Xin-Yuan Song, Haijin He, Bin Lu
Publication date: 26 January 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2021.107365
Cites Work
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- Cure events in default prediction
- Mixture cure models in credit scoring: if and when borrowers default
- Bayesian hypothesis testing in latent variable models
- An introduction to copulas.
- Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk
- Additive risk model for current status data with a cured subgroup
- A prediction-driven mixture cure model and its application in credit scoring
- Estimation and Model Selection for Left-truncated and Right-censored Lifetime Data with Application to Electric Power Transformers Analysis
- Bayesian analysis of multi-group nonlinear structural equation models with application to behavioral finance
- A non‐default rate regression model for credit scoring
- Basic and Advanced Bayesian Structural Equation Modeling
- Latent Variable Models with Mixed Continuous and Polytomous Data
- A zero-inflated non default rate regression model for credit scoring data
- Equation of State Calculations by Fast Computing Machines
- A new mixture cure model under competing risks to score online consumer loans
- Bayesian analysis of the factor model with finance applications
- Regression Analysis of Additive Hazards Model With Latent Variables
- Monte Carlo sampling methods using Markov chains and their applications
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