At Thu, 15 Mar 2007 15:39:56 -0800, Ben Klemens wrote: > The same holds for the kurtosis and skew: if you have a sample and not a > population, then the unbiased estimate is of the form \sum(...)/(n-1). But > the above starts with 1/n, meaning we have population kurtosis normalized > by sample variance squared. > > If we have to choose only one kurtosis and skew function, it should > probably be the sample and not the population version. The fix is trivial: > just return kurtosis * n/(n+1.0) at the end of kurtosis_m_sd, and > similarly for skew.
Hello, I originally looked at the formulas for unbiased estimators of skewness and kurtosis and they were pretty complicated, so I went with the simple definition used by Octave & Matlab. -- Brian Gough
