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Visualising 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.
Written by: Stephen RichardsTags: Filter information matrix by tag: data validation, Filter information matrix by tag: missing data, Filter information matrix by tag: Nelson-Aalen

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.
Written by: Stephen RichardsTags: Filter information matrix by tag: missing data

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.
Written by: Stephen RichardsTags: Filter information matrix by tag: cause of death, Filter information matrix by tag: missing data

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.

Written by: Iain CurrieTags: Filter information matrix by tag: missing data, Filter information matrix by tag: mortality projections, Filter information matrix by tag: age extrapolation

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. 
Written by: Stephen RichardsTags: Filter information matrix by tag: survival models, Filter information matrix by tag: force of mortality, Filter information matrix by tag: GLM, Filter information matrix by tag: missing data

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.
Written by: Stephen RichardsTags: Filter information matrix by tag: postcodes, Filter information matrix by tag: geodemographics, Filter information matrix by tag: smoking, Filter information matrix by tag: missing data, Filter information matrix by tag: P-squared

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.

Written by: Stephen RichardsTags: Filter information matrix by tag: early retirement, Filter information matrix by tag: missing data, Filter information matrix by tag: Kaplan-Meier