Understanding Generalized Linear Models (Logistic, Poisson, etc.)

Good video but the background music is obnoxious and totally unnecessary

So if I have only one poisson distributed independent variable and one poisson distributed dependent variable, they have a linear relationship, should I be using 'poisson' distribution as the random component, and 'identity' as my link function? In MATLAB: glmfit(x, y, 'poisson', 'link', 'identity');

How about inverse binomial and tweedie distribution? Can you make a video?

Thank you. Clear explanation. Can we use GLM when observations are dependent or correlated? Or is it a situation where GLMs not applicable?

What happens if you have a mixture of variable types. Continous, discrete etc.

very concise video
very.
concise

i love this teacher

its a great video, thank you. but can i ask you some question, if i use poisson with 2 predictors, can i make it into plot diagram? sorry for my bad english, im from indonesia

Your value is more than your appearance
You are amazing.
Thanks for rapping me to the point of the truth regarding GLM

This is the best video I have ever watched on the Internet. Thank you so much for sharing your insights with the research community. God bless you, sir!!!

Are link functions a special case of activation functions (in the context of NNs)?

This is was very nice, had a nice laugh but very educational too, lmao

Thank you!

I was surprised at how complex problems can be solved with a simple two-layer feedforward binary classification neural network. With a single hidden layer with a ReLU activation function, followed by an output layer with a sigmoid activation, it is able to learn very complex binary classifications (Such as learning financial signals). Unfortunately, I did not see any tutorials on financial data modeling using linear layers - most are using CNN, LSTM, and GRU model types. Those model types just don't seem to learn my dataset as well as this two-layer feedforward binary classification neural network does.

Fun topic!

Not me giggling about your "Poisson" pronunciation in my office. Didn't know GLMs could be so funny.

Good Video

wow you are annoying af

Heyy, thank you for your great video!! I have a question on the difference between transformations and link functions.
Is it right that this shouldn't be the same?
mean(log(y))
log(mean(y))
And this should be the same?
mean(log(predict(mod)))
log(mean(predict(mod)))
If yes, why is this the case?
Thank you a lot!

难以置信的好视频！我能够感觉到他是真的懂

This was so great, thanks!!

Great explanation on the GLMs. It gave me some new insights for sure. May you keep growing! Thanks for the video. I guess I'm gonna land at your channel quite often :)

You are great! And I love music in the background, gives a crazy feeling which eases up information for some reason.

Quick question for you, if you're still checking these comments! When taking the next step and moving up to GLMMs because of the requirements of data structure, is it a necessity to still use a link function in your code? Thanks, love your videos

This is what I always need, someone explaining things with some fun and at the same time in dummie terms xd

The video plots a density for a Poisson distribution, but a Poisson distribution is discrete. Thus such a density plot is just a rough approximation of the probability mass function of a Poisson distribution.

I use a generalization of Poisson regression called inhomogenous Poisson point process regression. It is useful for modelling arrivals of discrete units into a system over time.

The residuals from the conditional mean from a gamma generalized linear model will not be gamma-distributed. A quick way to confirm this is to realize that the outcome variable is sometimes less than the predicted mean value, resulting in a negative residual. But a gamma distribution has non-negative support, and therefore cannot be the distribution of the residuals. In general the residuals do not follow the same distribution as the likelihood.

The negative binomial distribution is obtained by the compound distribution of a Poisson distribution with Gamma-distributed inter-arrival times. It generalizes the Poisson distribution to have over-dispersion (i.e. the mean being less than the variance). The negative binomial cannot give underdispersion where the variance is less than the mean, but this can be achieved using the generalized Poisson distribution.

Man u are an amazing teacher

Great explanation, it put so many things I had in mind in the right order. Sub. Thank you!

Wow. Fun. Thanks learned a lot without getting bored

Thanks for your explanation! If you have some examples how to apply them, it would be extremly helpful! Thanks a lot.

Just gave a couple of likes 😂

GLMM video? & Beta distribution please

i wish every professor was like you. how you kept my attention was amazing.

Honestly, thank you so much for this explanation!! It's super super helpful to have someone actually explain the different types of glm's in a easy to understand way. I had not idea what they were nor when to use them, and now I don't have to keep bashing my head against a wall trying to understand the world of statistics :)

This was great

as an ecologist in progrees I can say, in ecology EVERYONE is using GLM all the time even when they could be using other simpler methods so here I am trying to actually understand them ahjhahaha

Are you teaching statistics in COMIC SANS??

Extremely helpful video ! Thank you for your clear explanations

So can we conclude that "tobit models, truncated models, and the heckmann model( tobit II model) follow a Gamma distribution?

Guy! You're amazing. Good job!

No Taylor series expansion?

Great stuff as usual. Keep up.

First 100K views. Congrats! Keep it on.

Man this video was great. I do get the excitement for GLMs tho, i actually got significant results using that and not a student T as suggested by my tutor.

Nice content. Would be better with less music and distractions.

Really great video, thanks