Significantly enhancing your models

(Apr 13, 2020)

In building a mortality model (or any other kind of risk model) it is usually best to build a single, over-arching model rather than split the data into sub-groups (an approach called stratification, the disadvantages of which are discussed in Macdonald et al (2018)).  One obvious reason is to reduce the total number of parameters: why fit two parameters for age when one will do?  Another reason is that a larger data set will have more power and thus smaller standard errors. In the example discussed here we merge the data sets of two pension schemes to gain more statistical power when measuring shared risk factors such as age and gender.

However, a less obvious reason not to stratify is that the single model can leadů

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Tags: enhancement, concealment, stratification

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