In population studies, it is customary to calculate the mortality rates as the ratio of the
number of deaths to the number of exposures to risk for a given set of ages and calendar years. The
demographic data sets usually involved are sometimes affected by some problems, i.e. missing data or
high volatility. In order to better manage these problems, we propose to represent the mortality process
as a Gaussian random field and to use the Kriging methodology for building up mortality values. We
analyze the quality and the flexibility of this approach into some demographic applications based on
Italian mortality data.
