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When fitting a mortality model, analysts are faced with the decision of which risk factors to include or exclude. One way of doing this is to look for the improvement in an information criterion that balances the fit against the number of parameters. The bigger the improvement in the information criterion, the more strongly the model with the smaller value is preferred.
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.
Factors
In statistical terminology, a factor is a categorisation which contains two or more mutually exclusive values called levels. These levels may have a natural order, in which case the variable is said to be an ordinal factor.
Degrees of freedom
In an earlier post questioning whether we still need standard tables, we used the AIC to choose between models.
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.
Choosing between models - a business view
We discussed how we use the AIC to choose between models.