# Find maximum likelihood estimation (MLE) for theta and prove that it maximizes likelihood function.?

Given X1,X2,...Xn=f(x;theta), where:
f(x;theta)=(theta)(x)^(theta-1) for 0<x<1 and 0<theta<infinity
=0 for otherwise
Find maximum likelihood estimation (MLE) for theta and prove that it maximizes likelihood function.
The final answer is -n/(theta^2)
I...
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Given X1,X2,...Xn=f(x;theta), where:

f(x;theta)=(theta)(x)^(theta-1) for 0<x<1 and 0<theta<infinity

=0 for otherwise

Find maximum likelihood estimation (MLE) for theta and prove that it maximizes likelihood function.

The final answer is -n/(theta^2)

I would really appreciate the steps explained thanks.

f(x;theta)=(theta)(x)^(theta-1) for 0<x<1 and 0<theta<infinity

=0 for otherwise

Find maximum likelihood estimation (MLE) for theta and prove that it maximizes likelihood function.

The final answer is -n/(theta^2)

I would really appreciate the steps explained thanks.

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