Mathematical Statistics (2024): Lecture 11

Convergence in Probability (continued) and Convergence in Distribution

Let's try this again. The last video that I posted titled "Lecture 11" was not actually the correct video! (Thank you to the viewer that pointed this out. I'd love to give you a shout out for that but I lost all comments when I took down the incorrect video. 🙁)

In this video:
🔹 The Continuous Mapping Theorem (for convergence in probability) 2:23
🔹 Joint Convergence in Probability to Deal With Sums 10:35
🔹 The Sample Variance! 14:38
🔹 Convergence in Distribution, Definition 31:30
🔹 The CDF of a constant 34:50
🔹 CDFs are Right Continuous 36:55
🔹 Convergence in Distribution, Examples 40:17
🔹 Convergence in Probability is Stronger 59:42

New videos release every Tuesday and Thursday!

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Thanks for watching! Consider checking out my MathStat textbook!

Also, if you are interested in data science, check out my courses on Coursera!

[AbhijitGuptamjj]

Thanks for uploading your fantastic lectures. I am revisiting Math Stats after a while andI I am glad I stumbled across this hidden gem. I really like your way of explanation.

[madhavpr]

Hi Professor. I commented something like "Where's measure theory?" on the deleted video. What I actually meant was- it has been 3 months since we have seen measure theory. :( :(

As far as your math stat videos are concerned, I have nothing else to say except this- Your teaching style makes math stat fun and enjoyable. :D

[TheTacticalDood]

Thanks for the upload. You are the best! One question, though, how would you recommend practising these concepts?