Tell me more ×
Cross Validated is a question and answer site for statisticians, data analysts, data miners and data visualization experts. It's 100% free, no registration required.
up vote 1 down vote favorite

I have samples of Bernoulli distributed variable that are neither the first nor the second i in iid. My goal is to model their sum.

I got from Wikipedia that I can use the poisson binomial distribution to make up for one of the i's, but then I have to keep all the inidividual probabilities.

It would probably also be possible to throw the central limit theorem against it somehow to model it as a Gaussian, but I wonder if I can do better.

Are there any results on how well a binomial distribution fits the sum of non identically non independently distributed Bernoulli samples. Especially if I can get some bounds on the accuracy wrt the correlation of the samples or something like that.

share|improve this question
1  
See this thread: stats.stackexchange.com/questions/5347/… – onestop Jan 21 '11 at 16:05
1  
The answer depends, in an essential manner, on precisely how your observations depart from independence and how they fail to be identically distributed. In general the binomial distribution, with a single parameter, is too "rigid" to model your sum: you should be looking at families with more parameters to allow for under- or over-dispersion. Perhaps you could tell us more about these data? – whuber♦ Jan 21 '11 at 17:31

Know someone who can answer? Share a link to this question via email, Google+, Twitter, or Facebook.

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.