Take the 2-minute tour ×
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It's 100% free, no registration required.

Recently I ran into a test using both zipf's and self-similar generated datasets. I followed the description from Jim Gray's paper on generating such datasets (Quickly Generating Billon-Record Synthetic Databases). In that paper it mentions:

It is commonly thought that self-similarity and the Zipf distribution are the same, or at least close. This misconception ... In particular, other statistics can yield very different results for the self-similar and Zipf distributions.

So my question is, from a statistic point of view, what is the actual difference between these two distributions intuitively? I am not a statistician, but (probably wrong) my thought is that they are all some power-law shaped distribution...

Just clarify: when I am talking about "the same", I am meaning that there is a (mathematic or statistic) way to bridge these two distributions... or one can be converted (mapped) into another...

share|improve this question
add comment

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.