My dataset contains two (rather strongly correlated) variables $t$ (runtime of algorithm) and $n$ (number of examined nodes, whatever). Both are strongly correlated by design, because the algorithm can manage roughly $c$ nodes per second.
The algorithm was run on several problems, but it was terminated if a solution hasn't been found after some timeout $T$. So data is right-censored on the time variable.
I plot the estimated cumulative density function (or the cumulated count) of variable $n$ for the cases where the algorithm did terminate with $t<T$. This shows how many problems could be solved by expanding at most $n$ nodes and is useful for comparing different configurations of the algorithm. But in the plot for $n$, there are those funny tails at the top going right sharply, as can be seen in the image below. Compare the ecdf for variable $t$, on which the censoring was done.
Cumulated Count of $n$
Cumulated Count of $t$
I understand why this happens, and can reproduce the effect in a simulation using the following R code. It's caused by censoring on a strongly correlated variable under the addition of some noise.
qplot( Filter(function(x) (x + rnorm(1,0,1)) < 5, runif(10000,0,10)), stat="ecdf",geom="step")
How is this phenomenon called? I need to state in a publication that these fans are artifacts of the experiment and do not reflect the real distribution.