Estimation of the parameters of generalized linear models in the analysis of actuarial risks

Methods of estimating the parameters generalized linear models for case paying insurance premiums to clients are considered. The iterative-recursive weighted least squares method, Adam optimization algorithm, and Monte Carlo method Markov chains were implemented. Insurance indicators target variable randomly generated due problem public access data. For latter, normal exponential law distribution Pareto with corresponding link functions used. Based on quality metrics model learning, conclusions made regarding their construction quality.