  A number of models for generating statistical data in various fields of
insurance, including life insurance, pensions, and general insurance have been
considered. It is shown that the insurance statistics data, as a rule, are
truncated and censored, and often multivariate. We propose a non-parametric
estimation of the distribution function for multivariate truncated-censored
data in the form of a quasi-empirical distribution and a simple iterative
algorithm for its construction. To check the accuracy of the proposed
evaluation of the distribution function for truncated-censored data, simulation
studies have been conducted, which showed its high efficiency. The proposed
estimates have been tested for many years by the IAAC Group of Companies in the
actuarial valuation of corporate social liabilities according to IAS 19
Employee Benefits. Apart from insurance, some results of the work can be used,
for example in medicine, biology, demography, mathematical theory of
reliability, etc.
