Model construction and evaluation are two important aspects of the empirical implementation of loss models. To construct a parametric model of loss distributions, the parameters of the distribution have to be estimated based on observed data. Alternatively, we may consider the estimation of the distribution function or density function without specifying their functional forms, in which case nonparametric methods are used. We discuss the estimation techniques for both failure-time data and loss data. Competing models are selected and evaluated based on model selection criteria, including goodness-of-fit tests.

Computer simulation using random numbers is an important tool in analyzing complex problems for which analytical answers are difficult to obtain. We discuss methods of generating random numbers suitable for various continuous and discrete distributions. We also consider the use of simulation for the estimation of the mean squared error of an estimator and the p-value of a hypothesis test, as well as the generation of asset-price paths.