Posted by James Youngman on June 29, 1998 at 08:29:38:

I have an application in which a least-squares fit

needs to be done online (i.e. without storing the

whole input vector). I have somple code to do this.

It turns out that I also need to perform

extreme-outlier rejection and I don't know how to

do that (online).

Details: Input data is linear, gradient may be + or

- but the sign is known in advance. N is between 10

and 500,000. The noise is normal, and the signal to

noise ratio varies between roughly 1e-4 and 1e0.

Where the noise ratio approaches 1e0 N approaches

500,000.

As well as the normally distributed noise, there are

rogue measurements which are the only ones that we

must reject. They're wild. We we measure S.D. of the

successive y deltas having rejected these outliers

by eyeball, the measurements to be rejected lie at

Z values in excess of (say) 50.