CS2 – Risk Modelling and Survival Analysis
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IFoA (Institute and Faculty of Actuaries) の学習者向けの無料CS2 – Risk Modelling and Survival Analysis学習ノート。各章のリンクから、要点・例題・練習問題への導線にアクセスできます。
Random variables and distributions for risk modelling
Loss distributions, with and without risk sharing
Compound distributions and their applications in risk modelling
Introduction to copulas
Introduction to extreme value theory
Time series
Understand the core concepts underlying time series models
Applications of time series models
Stochastic processes
Stochastic processes
Understand and apply a Markov chain
Define and apply a Markov process
Survival models
Concepts of survival models
Understand the estimation procedures for lifetime distributions
Derive maximum likelihood estimators for transition intensities
Transition intensities dependent on age (exact or census)
Graduation and graduation tests
Mortality projection
Machine learning
Bias/variance trade-off and relationships with model complexity
Cross-validation to evaluate models on unseen data, and estimate hyper-parameters
Understand how regularisation can be used to reduce overfitting in highly parameterised models
The use of software to apply supervised learning techniques, to solve regression and classification problems
The use of metrics such as precision, recall, F1 score and diagnostics such as the ROC curve and confusion matrix to evaluate the performance of a binary classifier
Unsupervised learning techniques (principal component analysis, K-means clustering) to reduce data dimensionality, identify latent substructure and detect anomalies
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