Defining an empirical cumulative distribution is straightforward - just calculate the percentiles or ranks for your data. However, what about an empirical or data-driven probability density functions? Here, kernel density estimation (KDE) comes to the rescue. Today, we are discussing this method, its assumptions and parameters, and applying it to calculate the kernel density of S&P 500 stock returns probability distribution.

Don't forget to subscribe to NEDL and give this video a thumbs up for more videos in Statistics!

Please consider supporting NEDL on Patreon:   / nedleducation