Propagation of errors with median absolute deviation from the median? Is there a theoretically-sound way to perform propagation of errors with robust statistics? I am trying to characterize the errors inherent in a measurement and propagate the uncertainty through calculations involving that estimate.
In the past, I have assumed that my errors are normally distributed and then used uncertainty propagation. However, this is clearly a bad assumption (my data is symmetric but guaranteed to have significant outliers), and I am intrigued by using a measure like Median Absolute Deviation.
Is it valid to treat MAD like a standard deviation? Is there a different measure I should be using for this application? 
 A: Beware your acronyms; MAD has been used for more than one thing.
I assume you mean 'median absolute deviation from the median'; speak up if you meant one of the other MADs.
Variances (and hence s.d.'s) have some nice properties. e.g. sums of r.v.s have nice simple variance results.
MAD doesn't have a result like that.
Consider $X_1, X_2$ both take the value $-1$ with probability $1/3$ and $1$ with probabiity $2/3$, while  $Y_1$ and $Y_2$ take the values of $-1$, $0$ and $1$ with probability $1/3$, and $Z_1$ and $Z_2$ take the values of $-1$ and $1$ with probability 0.1 each and $0$ with the remaining probability (0.8). All variables are independent.
The $X$ and $Z$ variables have a MAD of 0. The $Y$'s have a MAD of 1.
$X_1 + X_2$ has a MAD of 2.  (0,0) -> 2
$Z_1 + Z_2$ has a MAD of 0.  (0,0) -> 0
$Y_1 + Y_2$ has a MAD of 1.  (1,1) -> 1
There's clearly no such neat relationship.
MAD's of linear rescalings will scale - MAD(2X+3) = 2 MAD(X), but most other manipulations don't behave in neat ways.
