Brilliant! Thank you so much for the playlist so far. This video really helps link the prior more general theory to a more concrete example. However, I was wondering if you would also be showing how the variances of the betas are estimated for this model in this framework, and also whether a similar video would be possible where you focus on illustrating this framework for a GLM where the link isn't the identity and the weights aren't 1, so it's clearer how those more flexible parts work when "actually needed"?
Thanks so much for this comment!! The holidays are slowing me down a bit with adding more to this playlist, though my next few videos will be a Bernoulli distribution (Yes/No, or 0,1, outcomes) example, where the weights will not be 1. I will also use R to provide an example for how to calculate the estimates of beta. Regarding estimating the variance for the beta estimates - this is a very good point - I have not read ahead yet on the Nelder paper to know if they cover this topic (I would hope that they do…) However, if they don’t, I will definitely add a video at some point on this - estimating beta is not very useful, unless we also have an idea about its variance.
Here are the links to the bernoulli example: Step 1 (Show Bernoulli is from Exponential family): ua-cam.com/video/8KJJbek3I6g/v-deo.html Step 2 (find the link function and the weights and y for weighted Least Squares): ua-cam.com/video/ak_3dC0pJes/v-deo.html Step 3 (wrap up and program everything in R to show that you get the same thing as their GLM function): ua-cam.com/video/lHIKoLUHzAk/v-deo.html
@@Stats4Everyone I hope you had a lovely holiday and happy New Year! The new videos are fantastic. You're a stats star. Especially showing how you can implement it in R. If it's possible to eventually add a video/videos on estimating the variances that would be amazing. You could obviously use bootstrapping to estimate confidence intervals (or permutation methods to estimate p-values, if you really care about them), but it would be great to see where the analytical SEs come from.
Brilliant! Thank you so much for the playlist so far. This video really helps link the prior more general theory to a more concrete example. However, I was wondering if you would also be showing how the variances of the betas are estimated for this model in this framework, and also whether a similar video would be possible where you focus on illustrating this framework for a GLM where the link isn't the identity and the weights aren't 1, so it's clearer how those more flexible parts work when "actually needed"?
Thanks so much for this comment!! The holidays are slowing me down a bit with adding more to this playlist, though my next few videos will be a Bernoulli distribution (Yes/No, or 0,1, outcomes) example, where the weights will not be 1. I will also use R to provide an example for how to calculate the estimates of beta. Regarding estimating the variance for the beta estimates - this is a very good point - I have not read ahead yet on the Nelder paper to know if they cover this topic (I would hope that they do…) However, if they don’t, I will definitely add a video at some point on this - estimating beta is not very useful, unless we also have an idea about its variance.
Here are the links to the bernoulli example:
Step 1 (Show Bernoulli is from Exponential family): ua-cam.com/video/8KJJbek3I6g/v-deo.html
Step 2 (find the link function and the weights and y for weighted Least Squares): ua-cam.com/video/ak_3dC0pJes/v-deo.html
Step 3 (wrap up and program everything in R to show that you get the same thing as their GLM function): ua-cam.com/video/lHIKoLUHzAk/v-deo.html
@@Stats4Everyone I hope you had a lovely holiday and happy New Year!
The new videos are fantastic. You're a stats star. Especially showing how you can implement it in R.
If it's possible to eventually add a video/videos on estimating the variances that would be amazing. You could obviously use bootstrapping to estimate confidence intervals (or permutation methods to estimate p-values, if you really care about them), but it would be great to see where the analytical SEs come from.
@@HamiltonHammy-g6w yup. That is definitely on my radar for another topic to cover in this glm playlist :)