The derivative of f_theta(X_i) is the RV. A function of a RV is itself a RV. Many thanks for watching. Don't forget to subscribe and let others know about this channel.
Many thanks for watching. Don't forget to subscribe and let others know about this channel. Don't forget about the chain rule when you are taking a derivative. (f'f^{-1})' = f''f^{-1} + f'(-f^{-2})f'
Here is my missing peace i would like to share with thouse who are watyching 6:44 $-\mathbb{E}[\ell'^2]=-\text{Var}(\ell')$ why? We should take a look the Variance definitione. $\text{Var}(\ell')=\mathbb{E}[\ell'^2]-(\mathbb{E}[\ell'])^2$ Since the $(\mathbb{E}[\ell'])^2=(0)^2=0$ Then $\text{Var}(\ell')=\mathbb{E}[\ell'^2]$ I always forget about that property of the variance!
YESSSS THANK YOU! can’t tell you how grateful I am... was ready to fully give up on this exam. Never been able to find this actually explained lol
Makes my day to hear this! Thanks for watching and good luck on your exam. Let us know how you do on the exam.
statisticsmatt it went well all things considered, thanks again :)
Good Job!
This is very easy to understand! Thank you so much!
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Thank you so much for the video!! Just to confirm, at 8:17, f_theta(X_i) is a R.V. , where f_theta is the pdf of X?
The derivative of f_theta(X_i) is the RV. A function of a RV is itself a RV. Many thanks for watching. Don't forget to subscribe and let others know about this channel.
Variance is a function of score function or sample size
Will you point out where in the video is your question coming from? Many thanks for watching and don't forget to subscribe.
legend
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4:46 why isn't it l'' = f''/f - f'/f²
why would one square f' in the second term?
Many thanks for watching. Don't forget to subscribe and let others know about this channel. Don't forget about the chain rule when you are taking a derivative. (f'f^{-1})' = f''f^{-1} + f'(-f^{-2})f'
Here is my missing peace i would like to share with thouse who are watyching 6:44
$-\mathbb{E}[\ell'^2]=-\text{Var}(\ell')$ why?
We should take a look the Variance definitione.
$\text{Var}(\ell')=\mathbb{E}[\ell'^2]-(\mathbb{E}[\ell'])^2$ Since the $(\mathbb{E}[\ell'])^2=(0)^2=0$ Then $\text{Var}(\ell')=\mathbb{E}[\ell'^2]$
I always forget about that property of the variance!
Many thanks for watching and sharing your thoughts! Much appreciated. Don't forget to subscribe and let others know about this channel.