Robust standardization of data I have some data where I want to determine whether the shape of the probability distribution has changed compared to 10 years ago.
One example is that I have for various automobiles multiple measures of price at a given point in time from different used car dealers and online sellers.
make, model, year of manufacture, price
(there are further complications such as condition etc, but I've simplified the problem somewhat).
The data has a number of outliers. Is it possible to do a robust (to clarify, by robust I mean resistant to outliers) transformation that preserves the original distribution of values, but allows a comparison of probability distributions (with different means / sds) on a similar or identical scale such as [0,1].
Also, if I had an hypothesis such that maybe the distribution of prices had a different shape in the past. Is there any sensible way to combine the data
from different makes and models so as to get a more accurate estimation of the shape of the distribution function, or is this completely nonsensical.
I can of course individually compare the same make, model, yr of manufacture but I'm looking at a large number of comparisons.   
 A: Of course, it is possible to rescale your multivariate data so as
to express it in some standardized basis. It is also possible to 
do that in a way that preserves the distribution of your data (read 
the correlations between your original variables). 
Finally it is also possible to do this standardization 
in a way that is not influenced by the outliers. Furthermore, you will 
 get the added benefit that it will also reveal potential outliers as 
standing out from the main body of your data. 
denote $\pmb X$ your $n$ by $p$ data matrix. Let $(\hat{\pmb\mu},\hat{\pmb\varSigma})$ be
respectively, the robust estimate of location and scatter of $\pmb X$. You can
 then simply transform your data as 
$$\pmb Z=\hat{\pmb\varSigma}^{-1/2}(X-\pmb1_n'\hat{\pmb\mu})$$
where $1_n$ is a $n$-vector of ones and $\pmb D^{-1/2}$ denotes the matrix inverse square root of $\pmb D$. This transformation preserves the distribution of your data, 
standardizes it and makes the outliers stand out.
To compute $(\hat{\pmb\mu},\hat{\pmb\varSigma})$, there are several 
algorithms, but they all do essentially the same thing. The simplest, 
oldest is the FastMCD. You will find an R  (open source), good implementation
 of it (as well as that of some other competitors) here. Make sure to read the vignette: it's very well done. 
You can of course also use $(\hat{\pmb\mu},\hat{\pmb\varSigma})$ to derive a measure of outlyingness in $[0,1]$ associated with each observations (telling you how much that observation departs, in the multivariate space, from the bulk of the data). The way to compute such an outlyingness index is to do: 
$$\text{Out}(\pmb x_i)=\left(1+(\pmb x_i-\hat{\pmb\mu})'\hat{\pmb\varSigma}^{-1}(\pmb x_i-\hat{\pmb\mu})\right)^{-1}$$
where $\pmb x_i$ is the i-th observations. You can use this index as a form of center outward ranking of your data. Of course, this measure will also not be influenced by the outliers and so you can also use $\text{Out}(\pmb x_i)$ to reveal them (e.g. the outliers will have stand out by having a very low values of $\text{Out}(\pmb x_i)$).
A: The only standardizations that ensure the same limits are those based on the minimum and maximum such as (value $-$ minimum) / (maximum $-$ minimum) but these would usually be considered far from robust, if robust here means resistant to outliers, which is far from the only sense. 
(value $-$ mean) / SD is very commonly used, but again that is not robust in the usual sense. (value $-$ median) / IQR is more robust. 
You may be working with some other definition of robust, in which case you would need to spell it out here. 
Your example of a hypothesis, "the distribution of prices had a different shape in the past", is rather general. 
On a variety of grounds, I suspect that your problem would be simpler if you used a quite different approach and looked at logarithm of price. That should pull in outliers. That's not completely arbitrary because it is so common to talk of prices in general rising (sometimes falling) by some percentage, which corresponds to a shift of log price by the corresponding constant. Much here depends on whether you, and especially your likely readership, are comfortable with thinking on a logarithmic scale. 
An alternative used by Howard Wainer for car prices is the reciprocal. An accessible summary of his article can be found at http://mahalanobis.twoday.net/stories/908248/ 
I'd expect that looking at a variety of graphs (histograms or even better quantile plots) would help to see what kinds of changes there have been to the distribution over time. 
A: No, it is not possible to do a robust transformation that preserves the original distribution of values.  This is because any robust transformation (e.g., Winsorizing) will necessarily change the distribution (e.g., if the highest and lowest parameters are removed, then this changes both the shape of the distribution and the degree of variation).
A simple way of achieving your desired outcome is, within each group that you wish to compute the distribution, compute z = floor(5 * (x - min) / (max - min + 0.000001)) and then compare the proportion of observations in each of the 5 categories.  Of course you could use more or fewer than 5 categories if you so wanted.
If you were concerned about a small number of very extreme observations you could first Winsorize the data.  
