Finding outliers in multiple dimensions I'm working on dataset which isn't normally distributed. It contains three dimensions: cost, discount and profit.
I'm trying to find outliers in all these dimensions. I used $\text{z-score}$ to find outliers in single dimension to find high cost causing outliers.
As a next step I tried to find outliers with high cost and high profit and low discount.
I came up with a formula of 
$$ \text{z-score}(cost)+\text{z-score}(profit)-\text{z-score}(discount) $$
(negative sign because I want to find outliers with low discount).
Is this approach meaningful? Or is there any further proven way to achieve this?
 A: The problem of using only z-score to determine outliers is that if works pretty okay if your variables are independent but they show bad results when they have high correlation between each other.
For example, in the plot below the blue circle is what you expect your normal data to be in. 
 
You have a new data plotted in green which you expect to be classified as an $outlier$. But according to a z-score analysis (plotted in pink) your new data is classified as $normal$.

To avoid this problem of interpretation you can use Anomaly Detection Systems for giving better predictions or - if you have a lot of data on your outliers - you can use Logistic Regression so it can adapt to what you are looking for.
A: If these three variables were independent then you wouldn't be asking this question, and simply used the z-score. So, I assume that there's significant correlation between the variables which is a cause of concern for you. You want a measure of outlierness that accounts for common movements of variables.
I suggest PCA. Run a PCA analysis, and analyze z-scores of principal components. They are independent by construction, so you can analyze their z-scores. The point is that you can apply a univariate outlier detection now.
Implementation details
The PC scores don't have to be normal, of course, so you may need to fit other than normal distribution to get p-values, which is a technicality. 
You may need to first transform the variables into stationary series. For instance, often cost variable in long series is not stationary due to inflation. In this case a common approach is to either look at the difference or log difference of cost. Once you dealt with non-stationarity, apply PCA and get the series of three scores.
