Choosing between models - a business view

We discussed how we use the AIC to choose between models. The standard definition of the AIC is:

AIC = -2 * log-likelihood + 2 * number of parameters

However, this is a statistician's view of a model, where the only criterion for including a parameter is whether it is statistically significant. A business view might be different, as each extra parameter in a system will cost you money. IT systems have to be specified, programmed, tested and maintained, for example, and IT staff are not cheap. Each extra parameter might therefore cost you £5,000 in development costs (say), so you might be inclined to only include parameters if they are really significant. One way of doing this is to increase the penalty for the number of parameters in the definition of the AIC as follows:

AIC = -2 * log-likelihood + N * number of parameters

where N can be 2 for a statistical view, as before. However, if you felt that complexity was expensive, you can be particularly demanding of extra parameters and set N to 3, 4 or 5 (say). Thus, the AIC is not only a tool for the statistician, but it can also be adapted to help make business decisions.

Written by: Stephen Richards
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AIC options in Longevitas

Longevitas users can set the AIC multiplier in the Modelling area of the Configuration settings. The AIC multiplier can be controlled from the Calculation Core section. 

Previous posts

Choosing between models

In any model-fitting exercise you will be faced with choices. What shape of mortality curve to use? Which risk factors to include? How many size bands for benefit amount? In each case there is a balance to be struck between improving the model fit and making the model more complicated.
Tags: Filter information matrix by tag: AIC, Filter information matrix by tag: log-likelihood, Filter information matrix by tag: model fit

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