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Topics include survival models concepts; estimation procedures for lifetime distributions; multiple state models; multiple decrements; binomial and Poisson model of mortality; actuarial applications of continuous‐time and discrete‐time Markov processes; exact and census methods for estimating transition intensities based on age.
Intended learning outcomes
On successful completion of this subject a student should be able to:
- Explain the concept of survival models;
- Describe estimation procedures for lifetime distributions;
- Define a discrete‐time Markov chain and discuss its actuarial applications.
- Define a continuous‐time Markov process, and apply Markov models in actuarial problems.
- Describe models of transfer between multiple states, including processes with single or multiple decrements, and derive relationships between probabilities of transfer and transition intensities.
- Derive maximum likelihood estimators for the transition intensities in models of transfers between states with piecewise constant transition intensities.
- Describe the binomial model of mortality, a maximum likelihood estimator for the probability of death and compare the binomial model with the multiple state models.
- Describe how to estimate transition intensities depending on age, exactly or using the census approximation.
High level of development:
- Written communication;
- Problem solving;
- Statistical reasoning;
- Application of theory to practice;
- Use of computer software
Last updated: 31 January 2024