How to do multiple regression where all dependent and independent variables have uncertainties?

I have some data that I would like to run multiple regression on. The important thing here is that I would like the multiple regression to take into account the uncertainties in each of the data points -- something that mvregress doesn't do, as far as I can tell. What's more, each measurement in each of the variables has an uncertainty associated with it. For example, each measurement y(i) in the dependent variable y has its own uncertainty sy(i).

y = [y(1) y(2) ... y(n)]; % measured values of y

sy = [sy(1) sy(2) ... sy(n)] % uncertainties in measured values of y

The same goes for each of the independent variables x, w, z, etc.

x = [x(1) x(2) ... x(n)]

sx = [sx(1) sx(2) ... sx(n)]

When doing a single linear regression analysis, the function york_fit is able to handle data that have uncertainties in both the measured x- and y-values. I'd like to use something analogous to that -- just for multiple regression.

Anybody know how to do that? Is there a variant on mvregress that does that? Are there any canned functions that do that?

-Ken