What is the best algorithm for computing covariance that would be accurate for a large number of values like 100,000 or more?

$\begingroup$ One quick note. I know some people will think this is basic but I have looked in a lot of places (numerical analysis texts, computational statistics texts and google scholar) and I've never seen it discussed. Doing the straightforward thing does not work for large datasets. $\endgroup$ – Kim Greene Nov 18 '09 at 20:26
Check out How to calculate correlation accurately. There are two common formulas that are algebraically equivalent but one has much better numerical properties than the other.

1$\begingroup$ Yes, but that is only a partial answer. The problem is that the “good” formula requires two passes over the data, one to compute the arithmetic means and one to compute the quadratic sums. It would be very handy to have an algorithm that uses only a single pass, yet is numerically stable. Surely, someone must have thought of one? It doesn't seem to me an intractable problem. $\endgroup$ – Harald HancheOlsen Nov 18 '09 at 21:53

2$\begingroup$ Here's a way to compute standard deviation in one pass. I imagine you could take the ideas from this algorithm to create an accurate singlepass algorithm for covariance. johndcook.com/standard_deviation.html $\endgroup$ – John D. Cook Nov 19 '09 at 0:17
The singlepass and parallel versions at Wikipedia may be what you're looking for. The single pass version is more numerically stable, but moves a division into the inner loop, which may hurt performance.
A single pass stable algorithm has been discovered in the time since this question has been originally answered:
Bennett, Janine, et al. "Numerically stable, singlepass, parallel statistics algorithms." Cluster Computing and Workshops, 2009. CLUSTER'09. IEEE International Conference on. IEEE, 2009.
An implementation is given in Boost.