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The Hermite model of mortality

In Richards (2012) I compared seventeen different parametric models for modelling the mortality of a portfolio of UK annuitants. The best-fitting model, i.e. the one with the lowest AIC, was the Makeham-Beard model:

\[\mu_x = \frac{e^\epsilon+e^{\alpha+\beta x}}{1+e^{\alpha+\rho+\beta x}}\qquad(1)\]

Written by: Stephen RichardsTags: Hermite splines, extrapolation

The Poisson assumption under the microscope

If you read almost any paper on modelling mortality you will find the assumption that the number of deaths follows the Poisson distribution.
Written by: Iain Currie

The cohort effects that never were

The analysis of cohort effects has long fascinated the actuarial community; these effects correspond to the observation that specific generations can have longevity characteristics different from those of the previous and the following ones. However, Richards (2008) conjectured that these cohort effects might be errors caused by sudden changes in fertility patterns.
Written by: Alexandre BoumezouedTags: cohort effect, mortality improvements

Mortality down under

Different countries have different mortality characteristics, and this is true even where countries have similar levels of wealth and development.  However, different countries also have shared mortality characteristics, and one of these is seasonal variation. 
Written by: Stephen RichardsTags: season, winter, cause of death

Is your mortality model frail enough?

Mortality at post-retirement ages has three apparent stages:

Written by: Stephen RichardsTags: late-life mortality deceleration, frailty, heterogeneity

Diabetes in the driving seat?

Those of us with an interest in population mortality find ourselves in proverbially interesting times. Established patterns of accelerating mortality improvements may have ended and we neither know precisely why this may have happened nor what will follow
Written by: Gavin RitchieTags: longevity, diabetes, mortality improvements

See You Later, Indicator

A recurring feature in my previous blogs, such as this one on information, is the indicator process:

\[Y^*(x)=\begin{cases}1\quad\mbox{ if a person is alive at age \(x^-\)}\\0\quad\mbox{ otherwise}\end{cases}\]

where \(x^-\) means immediately before age \(x\) (never mind the asterisk for now). When something keeps cropping up in any branch of mathematics or statistics, there are usually good reasons, and this is no exception. Here are some:

Written by: Angus MacdonaldTags: left-truncation, right-censoring, Poisson distribution

Compare and contrast: VaR v. CTE

Insurance reserving in many countries looks at extreme scenarios over a single year.
Written by: Stephen RichardsTags: conditional tail expectation, SST, quantile, percentile

Up close and intimate with the APCI model

This blog brings together two pieces of work. The first is the paper we presented to the Institute and Faculty of Actuaries, "A stochastic implementation of the APCI model for mortality projections", which will appear in the British Actuarial Journal. The second is a previous blog where I examined the role of constraints in models of mortality.

Written by: Iain CurrieTags: APCI, identifiability constraints

Senolytics: trials and judgements

In a previous post we considered the advantages of repurposing existing drugs to treat a new condition. The fact such treatments had already undergone safety testing and regulatory approval shortens the usually lengthy journey to the clinic.
Written by: Gavin RitchieTags: longevity, senescence