Kernel Density Estimation

[@ZinzinsIA]

concise and clear, yet enough to better understand and get an intuitive sense of what's joing on here. Thanks !

[@heydonShi]

Thank you for making this so clear.

[@vincentlabrecque2275]

Well explaned, great content. It would be cool to have a practical example of this tool in use. Currently learning Data Sciences with Python, and there's this tool available. I'm trying to develop intuitions on when to use KDE, and how it will help me gain insight on my data

[@econplustutoring9682]

This short video series is just spot-on and insightful in its execution! My only criticism is that you stopped haha (sorry for sounding unappreciative, I’m just a fan). I’m specialising in np in my Econ PhD - would you be able to do some material, eventually, on such things as Liu’s (1998) wild bootstrap or np iv regressions? I know it’s a bit heavy to request such, but I’m struggling to find good sources online to simplify those procedures.

[@iamZANIX]

Thanks

[@areejareej281]

Is kernel regression is another name of kernel ridge regression?

[@داوديحمزة-ق5و]

Thank you very beautiful, possible working example, on how to estimate the density function using the methode of kernel in the program R . Thanks

[@saurabhkale2778]

Thank you for the great explanation! But something is still unclear to me. The bandwidth that you mentioned, is it basically the decider of width of weighted data under consideration near the point for which we are predicting the value at?

[@superpronker]

That's certainly one way of thinking about it intuitively. In the case of the histogram, that's exactly what it is, but in the case of the nonparametric kernel density estimator, you should think of it more continuously: it is *how much* weight to put on the nearest observations relative to the farthest observations from a given point. This is because in the kernel estimator, we always put *some* weight on all observations, although the weight goes to zero as we move away from the point at which we are evaluating the density.

[@saurabhkale2778]

Okay. I got it. Thank You!

[@sonunitjsr223]

@@superpronker Nice explanation!

[@AmanSingh-xk2lv]

thanks!