(GDP)Renewing our mail-list

A short and simple administrative announcement ...

In common with many other organisations, we are celebrating the arrival of the EU General Data Protection Regulation (GDPR) by renewing our mailing list. We only use our mailing list for relatively infrequent communication about our blogs, research and software. We don't sell or pass on anyone's contact details.

In order to keep things simple, we are going to start from a clean slate. So, even if you had previously joined our mailing list, in this post-GDPR world, we're going to ask for you to reconfirm your desire to hear from us. If you don't do this, you won't receive mailshots from us again (but obviously can still find out what we are up to by visiting us here).

And, of course, once you are on our list, you can remove yourself at any time. You can either choose the "Remove" option from our "Contact" page, or simply ask us to remove you by email or by replying to any communication.

I told you it would be short and simple!

Written by: Gavin Ritchie
Publication Date:
Last Updated:

GDPR

Not all Longevitas services work at the individual level or process potentially personal data. However, the software incorporates a number of features and techniques to minimise the need for personal data even within individual modelling and rating calculations. Key features like "Transform on Download" and "Postcode Proxies" can anonymise postcodes, names and dates of birth. This retains the benefits of modelling individual lifetimes, but without uploading records that can identify individuals. And of course, our services operate strong authentication and encryption of uploaded data along with a variety of other technical measures. 

Previous posts

What's in a (file)name?

The upcoming EU General Data Protection Regulation places focus on the potential for personal data exposures to create a risk to the rights of natural persons. The best way to reduce such risk is to minimise the ability to identify individuals from the data you use in your analysis.
Tags: Filter information matrix by tag: GDPR, Filter information matrix by tag: data protection

Functions of a random variable

Assume we have a random variable, \(X\), with expected value \(\eta\) and variance \(\sigma^2\). Often we find ourselves wanting to know the expected value and variance of a function of that random variable, \(f(X)\). Fortunately there are some workable approximations involving only \(\eta\), \(\sigma^2\) and the derivatives of \(f\). In both cases we make use of a Taylor-series expansion of \(f(X)\) around \(\eta\):

\[f(X)=\sum_{n=0}^\infty \frac{f^{(n)}(\eta)}{n!}(X-\eta)^n\]

Tags: Filter information matrix by tag: GLM, Filter information matrix by tag: log link, Filter information matrix by tag: logit link

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