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Shouldn't the denominator in the partial sum in 1:09:26 be (1-\theta) and not (1-\theta^2)?

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is there any full course from you on the topic?

at the proof at 55:13, why cant we just take epsilon = 0 from the beginning?

No entendí la varianza de la ultima sumatoria, por que queda elevado a "2j" ?

Infinite thanks to you. I was desperately looking for a good material to learn time series analysis basics. The topics are explained amazing.

Too much theoretical stuff, too much complex notation. I'd prefer a straightforward explanation how SARIMA works (like "here are all the relevant formulas, here's an example-based explanation of the formulas).

you have a type error on the notes in symmetric difference

Thank you so much for a great explanation. Can you please also discuss regarding Yekutieli-Benjamini approach which considers dependency between the tests?

Sir, what book do you recommend for studying probabilistic measure theory?

Direct link to lecture notes so you don’t have to search: sites.ualberta.ca/~kashlak/data/stat571.pdf

At 1:14:25, why does the second equality follow from the first? While intuitive (the RHS is based upon a partition of E), its not clear to me why writing u*(E) in this manner is rigorously justified. We haven’t shown that u* is countably (or even finitely) additive, so why are we allowed break up u*(E) in this manner? Any help would be much appreciated!

Thank you so much 😊

You have bad handwriting :D but it is very useful content, Thank you.

Hi sir, I'm wondering why at around 1:17:00 we know we can extend nu to sigma(A2)? Doesn't this require that the measures are both sigma finite? I'm not sure whether that was assumed / clear

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Thank you sir for the lecture

thank you very much for the lecture. How is the dickeyt fuller test conducted ?

@45:50 up to scaling, obviously

Hello Professor, can you tell me how your board application is called? Thank you in advance!

Thank you from Egypt

The asymptotic result for AR(1) and MA(1) is different from the book by sigma^2 on the variance (page 134). BTW, thank you so much for sharing this few lectures. I was reading the book multiple times but having trouble really understanding the idea on top of all the maths presented. You really bring the fundamental idea out with all the explanations. I greatly appreciate it.

Big thank you from Germany!! Your lecture series really saves me while preparing for my exam. Your deep undestanding of measure theory shows in very undestandable explanations. Didn't find any other similarly understandable videos on this level of detail and formal correctness.

Thank you!

I appreciate that you provide intuition for new definitions and gently introduce them. btw, "outer measure" is spelled with just one "t" (unless it's a Canadian spelling I'm unaware of).

Thank you so much.

Realy clever explanation❤❤❤

Finally I got the measure theory. Thank you so much! Can you do the same with the Functional Analysis?

Excellent teaching. I am reading Stoffer’s book and hit a brick wall when it gets to spectral. You help me push through. Many thanks.

At around 1:26, you say mu-star "less than" but you write "greater than." I think you meant what you said. Yes?

It's interesting. Did you recommend any book in order to follow all playlist???

Prof Kashlak, excellent lecture. I believe at 40:27, it should be the union of [-i,-i-1]. Your union can be made disjoint if you use half-open intervals, [i,i+1), etc. , defining m([a,b})=m[[a,b]]=b-a. Thanks to you, your school for the great lecture, material. I know all this is obvious to you, guess yu just made a small mistake.

Wow these lectures are great! keep it up!

You're so clearly explained! Thank you so much for uploading this.

the best lectures on measure and probability theory I have seen, surprisingly because you give some varied proofs in your videos it pairs quite nicely with the springer textbooks in statistics "measure theory and probability theory" and the Oxford University lecture notes. The lectures are particularly good because you give some intuition into the subject which is needed sometimes, especially from a geometric point of view. These two things are not easy to come by with measure theory so therefore i appreciate that personally.

Your lectures are indeed viewed by people not in your class and I must say, you sir are a great lecturor.

Thank you so much! It's the first time that I understand what's going on!

WHAT A NICE INTRO. I will stay!

Hi, professor, any plan for further topics following probability and measure, such as stochastic process? I really enjoy your lectures!!

Il ilke the music in the beginning

Would there be a way of computing the order statistics for these t-statistics, re the tukey test?

Don't know that much about Canada, but , by being from a province, do you mean being born there? What if you move, at say, 2 years of age? Or move more than one? Maybe could do some sort of blocking in that regard? Or maybe assign to a person the province where they've lived the highest % of their lives?

Excelent lecture. In 16:11, by errors, it seems it would be residuals? If I may, pr ? of., a general question, if it makes sense, how to come up with the statistic to be used to do a given hypothesis test tests, e.g.,[ (n-1)^2*S_i]/Sigma^2 ? Or how did , e.g., Bartlett come up with his choice of statistic to test for equality of variances? Is there a general method do construct/define these statistics? Thank you.

Excellent! A bit on the longer side, but really helped to build intuition. Enjoyed all of it!

Excellent lecture! Thanks a lot.

I think for the inequality at 1:27:18 to hold we are assuming that the outer measure is continuous, which might still need some justification?

Moved over to this new channel. Once again, awesome can't wait to get through all the DOE videos.

Man this channel is awesome. Can't wait to go through al the videos. Thank you!