And here we go with the copula package in (the sandbox of) statsmodels! You can look at the code first here. I am in love with this package. I was in love with statsmodels already, but this tiny little copula package has everything one can hope for! First Impressions First I was not sure about […]
Read More Trying out Copula packages in Python – II
You may ask, why copulas? We do not mean this copulas. We mean the mathematical concept. Simply put, copulas are joint distribution functions with uniform marginals. The kicker, is that they allow you to study dependencies separately from marginals. Sometimes you have more information on the marginals than on the joint function of a dataset, […]
Read More Trying out Copula packages in Python – I
Every now and then, a data science practitioner will be tasked with making sense out of rare, extreme situations. And the good news is, there exist mathematical tools that can help you make sense of extreme events. And some of those tools are structured under a branch of probability which has (conveniently) been named Extreme Value Theory (EVT).
Read More Sympathy for the Extreme
Or “can daedalean words actually help make more accurate descriptions of your random variable? Part 1: Kurtosis Is a common belief that gaussians and uniform distributions will take you a long way. Which is understandable if one considers the law of large numbers: with a large enough number of trials, the mean converges to the expectation. […]
Read More Using higher moments to your advantage: Kurtosis