Random survival forests models for SME credit risk measurement
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Publication:398807
DOI10.1007/S11009-008-9078-2zbMath1293.62223OpenAlexW3124440722MaRDI QIDQ398807
Dean Fantazzini, Silvia Figini
Publication date: 15 August 2014
Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11009-008-9078-2
Applications of statistics to actuarial sciences and financial mathematics (62P05) Censored data models (62N01) Credit risk (91G40)
Related Items (4)
Forecasting SMEs' credit risk in supply chain finance with a sampling strategy based on machine learning techniques ⋮ Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model ⋮ The development of a simple and intuitive rating system under Solvency II ⋮ Discrete-time survival forests with Hellinger distance decision trees
Uses Software
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