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Posts feedVisualising data-quality in time
In a recent blog I defined the Nelson-Aalen estimate with respect to calendar time, rather than with respect to age as is usual.
Dealing with missing data
In an earlier post we looked at how to create a proxy for ill-health early retirements based on age at commencement. This is an example of dealing with missing data — we infer a useful proxy to replace the lost or missing health status at retirement.
Summary judgement
In previous posts we have looked at problems with the quality and reliability of cause-of-death data and a list of hurdles for mortality projections based on such data. One other issue is that of detail.
Forecasting mortality at high ages
The forecasting of future mortality at high ages presents additional challenges to the actuary. As an illustration of the problem, let us consider the CMI assured-lives data set for years 1950–2005 and ages 40–100 (see Stephen's blog posts on selection and data volumes). The blue curve (partly hidden under the green curve) in Figure 1 shows observed log(mortality) averaged over time.
Out for the count
In an earlier post we described a problem when fitting GLMs for qx over multiple years. The key mistake is to divide up the period over which the individual was observed in a model for individual mortality.
Sweating your data assets
In recent years insurers have looked to making better use of the data they already have. The appeal is simple: if you have already collected the data, then it is like leaving money on the table if it is not being exploited to the full.
Early retirements
Members of defined-benefit pension schemes can often retire early if they are in poor health. Unsurprisingly, such ill-health retirements exhibit higher mortality rates than those who retire at the normal scheme age.