The question is as follows, apologies in advance, I don't know how to do the LaTex thing in posts.
Let $X_1,\ldots,X_n, Y_1,\ldots,Y_n$ be independent random variables such that $X_i \sim N(\mu_1,\sigma^2)$ and $Y_j \sim N(\mu_2,\sigma^2)$. Both $\mu_1$ and $\mu_2$ are known but $\sigma^2$ is not.
Find the maximum likelihood estimator for $\sigma^2$ based on all $n+m$ observations. Show all working.
I am trying to work through with this but I am getting some horrible results when I get to the log-likelihood function. Any help in deriving the log-likelihood function would be appreciated.
Cheers.
homeworktag. Hint: Given observations $x_1,x_2,\ldots, x_m, y_1,y_2,\ldots,y_n$, replace each $x_i$ by $x_i-\mu_1$ and each $y_j$ by $y_j-\mu_2$. Now, you have $m+n$ observations of a zero-mean normal random variable of unknown variance $\sigma^2$. What is the likelihood function? What choice of value of $\sigma^2$ maximizes the likelihood function? Garnish and serve. – Dilip Sarwate Mar 11 at 2:59