Information Matrix

Recognising and quantifying mortality differentials is what experience analysis is all about. Whether you calculate traditional A/E ratios, graduate raw rates by formula (Forfar et al. 1988), or fit a statistical model (Richards 2012), the aim is always to find risk factors influencing the level of mortality.

The motto of the old UK Institute of Actuaries was certum ex incertis, i.e. certainty from uncertainty. I never particularly liked this motto — it implied that certainty can be obtained from uncertainty, whereas uncertainty is all-too-often overlooked.

An oft-overlooked aspect of statistical models is that parameters are dependent on each other. Ignoring such dependencies can have important consequences, and in extreme cases can even undermine assumptions for a forecasting model. However, in the case of a regression model the correlations between regressor variables can sometimes have some unexpectedly positive results.

In an earlier posting I listed some actuarial terms and their statistical equivalents (and later a short list of statistical terms and their equivalents in other fields).  Using different expressions for the same concept is an unfortunate barrier to understanding across disciplines.

The CMI recently asked for an overview note on survival models.  Since this subject is of wider actuarial interest, we wanted to make this publically available. An electronic copy can be downloaded from the link on the right.