00:01
Hello student.
00:02
So in part a we have to find the maximum likelihood estimator of theta that is theta hat.
00:09
So the given likelihood function for the distribution is like theta by x equal to twice x by root of theta or x vary from zero to infinity.
00:24
So first take a log on both side.
00:27
So it's log of theta by x equal to log of twice x upon root of theta.
00:37
After that take a derivative with respect to theta and set this equal to zero to find the maximum value.
00:44
So take a derivative with respect to theta.
00:56
So it's log of twice x upon root of theta.
01:02
So this become minus 1 by 2 plus 1 by 2 theta and equate this value equal to zero.
01:11
So it's minus 1 by 2 plus 1 by 2 theta equal to zero.
01:19
Now solving further it's 1 by 2 theta is equal to 1 by 2 shifting this value to the minus term and the theta estimated value comes to be 1.
01:36
We can say that so the mle of theta is 1.
01:48
So in part b even constant c for expectation c theta hat equal to theta.
02:04
We have to find the value of c...