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The Emperor's New Clothes, Part II

In my previous blog I described a real case where so-called artificial intelligence (AI) would have struggled to spot data problems that a (suspicious) human could find.  But what if the input data are clean and reliable?

Written by: Stephen RichardsTags: machine learning, neural networks, information criterion, AIC, BIC

Model selection

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.

Written by: Stephen RichardsTags: information criterion, AIC, relative likelihood

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.

Written by: Stephen RichardsTags: survival models, enhancement, AIC

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. 
Written by: Stephen RichardsTags: factor, categorical factor, ordinal factor, binary factor, AIC, optimisation

Degrees of freedom

In an earlier post questioning whether we still need standard tables, we used the AIC to choose between models.
Written by: Stephen RichardsTags: degrees of freedom, AIC, standard table

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.
Written by: Stephen RichardsTags: AIC, log-likelihood, model fit

Choosing between models - a business view

We discussed how we use the AIC to choose between models.
Written by: Stephen RichardsTags: AIC, log-likelihood, model fit