Suppose you have a bunch of independent time series each with binary outcomes A/B, all with the same CDF (but we don't know anything about it a-priori). We want to compute the average percentage of A outcomes, or better yet, the empirical CDF. The problem is that we only have information when the A outcome occurs. So if one time series has the following outcomes : B, B, B, A we are only informed when A occurs and when it does we know that 3 Bs occurred since the start / last A outcome.
Our naïve way of doing right now is to compute the percentage of A outcomes every time information is given, but my hunch is that the "delayed" information must bias that percentage.
Can someone point me in the right direction towards getting a statistically correct answer?