Sufficient Estimator | Factorization Theorem| 2 steps Rule to find the Sufficient estimator
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- Опубликовано: 22 авг 2024
- This video explains the Sufficient estimator with solved examples.
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I just wanted to take a moment to thank you for this incredible video. I'm currently studying this topic for my university course, and your clear and concise explanations have made a huge difference in my understanding. Your teaching style is engaging and informative, and it has helped me grasp concepts that I was struggling with before. I truly appreciate the effort you put into creating these educational videos. Thank you so much for sharing your knowledge and making learning enjoyable! ❣❣❣
Dear Professor, this is extremely helpful. Also, let me know if you upload the video on the ancillary and complete statistics including the Basu's theorem :)
this man about to save me a whole year of uni... great video mate
Very fully-explained and well-orgainized video!
in addition there is a smaller written error in 14:07: inside the right pink square on the third line, instead of lambda(x), it should be h(x).
I'm so happy I've discovered your channel! All the best.
Thanks... My pleasure. . keep sharing with others too.
thank you d/r keep going....!!! all your videos have good quality and thank you again.
Thank you sir. Learning from you is a blessing in disguise 🙏
Dear professor, this is extremely helpful for my stimation and detection course. Thank you very much!
Its my pleasure... Kindly share with other fellows too.
Hello, Dr. Garg. May I know which software you are using to display your steps after clicking? I see this is a good one that saves time and carries a good presentation.
Sir, it would be of great help, if you could pls cover ISI Mstat PYQ. You explain everything simply and that is very much needed there!
Thank you so much sir very very helpful 🙏....but when will we use pdf of order statistics to find the sufficient estimator for a parameter please do video on this topic 🙏
Thank you sir
Sir please basu theorem minimal sufficiency ka bhi gate pyq upload kriye
Very well explained sir
Thanks and welcome
Thank you sir from Bangladesh
assalam alaikum sir ..sir kindly upload a lecture on jackknife technique for reducing the amount of biasedness of a baised estimator.and some examples also including poisson distribution
thanku Sir ..your contents makes concepts crystal clear ..
is there any way to get your notes? I mean the screen slides
Thank you so much
Very helpful sir please upload video on sampling distribution
It is already available.... Check the playlist "Testing of Hypothesis " or "Probability&Statistics"
See here
Testing of Hypothesis: ruclips.net/p/PLO-6jspot8AIdl7W-Lh7PFsevgzCxNb3h
@@DrHarishGarg sir ye sab dekh liya aur videos upload kijiye estimation topic mai method of moments mle all these and testing of hypothesis mai most powerful test ye sab please 🙏
thanks alot, I have maximize some things
This and that!
super explanation
Hi sir, could you explain again as to ,how do you identify if an eq is unable to have a sufficient statistic ?
very useful for me sir
Thanks sir❤
Sir uniform distribution mein agar limit h 2theta
14:34 Sir pdf of gamma distribution of 2nd kind me beta denominator me nhi numerator me tha toh yha aapne denominator me kyu likha h ??
Sir plz reply
Agr normal distribution mei N(u, 1) diya ho toh sufficient efficient of mean kya hoga?.... Plzzz reply sir
sum(xi)
If not able to find h(x) you put it as 1. Then why not there in example 3? And what is the purpose of simply write 1? Is this only for a subjective exam.?
Thanks Alot but i have a confusion that when we will apply the T=max(x1,x2...xn) and when w use T=min(x1,x2,.....xn) order statistic???
Please ans me tomorrow is my exam.
Thats depends on the domain of x that whether x
@@DrHarishGarg Shukria 🧡
❤
Simply just reading the PPT only
Means u didn't understand it
5:46
The explanations for the examples was not good enough. I had to scratch my head a lot of times to understand a few and still have unresolved doubts over some.
Absolutely not
It is great and so simple ❤❤❤ thanks a lot sir
You remove a huge burden off my shoulder .❤❤❤
one of the worst explanation
Thank you sir
Thank you sir
Thank you sir