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Posts feedReducing uncertainty
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.
Out of line
Regular readers of this blog will be in no doubt of the advantages of survival models over models for the annual mortality rate, qx. However, what if an analyst wants to stick to the historical actuarial tradition of modelling annualised mortality rates?
Enhancement
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.
The ins and outs of bulk annuities
The UK has a well developed and highly competitive market in bulk annuities. These typically arise when a defined-benefit pension scheme wants to insure its liabilities.
Groups v. individuals
We have previously shown how survival models based around the force of mortality, μx, have the ability to use more of your data. We have also seen that attempting to use fractional years of exposure in a qx model can lead to potential mistakes. However, the Poisson distribution also uses μx, so why don't we use a Poisson model for the grouped count of deaths in each cell?
An early bath for the bathtub model
My last posting looked at why actuaries fitted survival models differently to statisticians, even though the conceptual framework for survival models is common to both disciplines.
Actuarial exceptionalism
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).
Why use survival models?
We and our clients much prefer to analyse mortality continuously, rather than in yearly intervals like actuaries used to do in previous centuries. Actuaries normally use μx to denote the continuous force of mortality at age x, and qx to denote the yearly rate of mortality. For any statisticians reading this, μx is the continuous-time hazard rate.
Survival models for actuarial work
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.
Business benefits of statistical models
In a recent meeting I was asked by a reinsurer what the advantages were of using statistical models in his business. The reinsurer knew about the greater analytical power of survival models, but he wanted more.