This open access book discusses the statistical modeling of insurance problems, a process
which comprises data collection, data analysis and statistical model building to forecast …

A phase-type distribution is the time to absorption in a continuous-or discrete-time Markov
chain. Phase-type distributions can be used as a general framework to calculate key …

In this paper we investigate the flexibility of matrix distributions for the modeling of mortality.
Starting from a simple Gompertz law, we show how the introduction of matrix-valued …

This chapter discusses state-of-the-art statistical modeling in insurance and actuarial
science, which is the generalized linear model (GLM). We discuss GLMs in the light of claim …

Phase-type distributions describe the time until absorption of a continuous or discrete-time
Markov chain (Bladt & Nielsen, 2017). The probabilistic properties of phase-type …

In this paper, we consider statistical estimation of time–inhomogeneous aggregate Markov
models. Unaggregated models, which corresponds to Markov chains, are commonly used in …

The task of modeling claim severities is addressed when data is not consistent with the
classical regression assumptions. This framework is common in several lines of business …

This paper addresses the task of modeling severity losses using segmentation when the
data distribution does not fall into the usual regression frameworks. This situation is not …

This chapter summarizes some techniques that use Bayes' theorem. These are classical
Bayesian statistical models using, eg, the Markov chain Monte Carlo (MCMC) method for …

The study of time-inhomogeneous Markov jump processes is a traditional topic within
probability theory that has recently attracted substantial attention in various applications …