Trukhan S.V.

Estimation of Generalized Linear Models Using Bayesian Approach in Actuarial Modeling

The article deals with Bayesian methodology for estimating unknown parameters of mathematical models and the method of analysis statistic data in insurance based on generalized linear models. These models are extension of linear regression when distribution of random variable can differ from normal. For estimating the parameters of proposed models classical and Bayesian approach were used. The main advantage of Bayesian approach is its ability to generate not only accurate estimates but probability distributions too.

Forecasting Actuarial Processes with Generalized Linear Models

The method for statistical data analysis in insurance based on application of generalized linear models is studied. These models are extension of linear regression when distribution of random variable can differ from normal however belongs to the class of elliptical distributions. The model constructed can be linear or non-linear (for example, logit or probit). For parameters estimation of the models proposed the generalized least squares (GLS) or the Markov chain Monte Carlo methods are used.