Season's Greetings to all our readers!

\[y = \frac{\log_e\left(\frac{x}{m}-sa\right)}{r^2}\]

\[\Rightarrow yr^2 = \log_e\left(\frac{x}{m}-sa\right)\]

\[\Rightarrow e^{yr^2} = \frac{x}{m}-sa\]

\[\Rightarrow me^{yr^2} = x-msa\]

\[\Rightarrow me^{rry} = x-mas\]

Written by: Stephen Richards
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Previous posts

Signal or noise?

Each year since 2009 the CMI in the UK has released a spreadsheet tool for actuaries to use for mortality projections. I have written about this tool a number of times, including how one might go about setting the long-term rate. The CMI now wants to change how the spreadsheet is calibrated and has proposed the following model in CMI (2016a):

\[\log m_{x,y} = \alpha_x + \beta_x(y-\bar y) + \kappa_y + \gamma_{y-x}\qquad (1)\]

Tags: Filter information matrix by tag: CMI, Filter information matrix by tag: APCI, Filter information matrix by tag: APC, Filter information matrix by tag: Lee-Carter, Filter information matrix by tag: Age-Period, Filter information matrix by tag: smoothing

Making sense of senescence

Historical research we discussed previously proposed that significant increases in average life expectancy would require the cure of multiple diseases of aging. Without considering the detail of cause-of-death calculations conducted more than two decades ago, it certainly seems implausible even now that we'll cross such a dramatic Rubicon in the near-term.
Tags: Filter information matrix by tag: longevity, Filter information matrix by tag: research, Filter information matrix by tag: inflammation, Filter information matrix by tag: disease

Comments

David Shaffer

06 January 2017

love the nerdy humour

yp^2 * new = (log(ear)+log(y))/log(ha)

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