# Lysario – by Panagiotis Chatzichrisafis

"ούτω γάρ ειδέναι το σύνθετον υπολαμβάνομεν, όταν ειδώμεν εκ τίνων και πόσων εστίν …"

## Archive for April, 2011

In [1, p. 61 exercise 3.7] we are asked to find the MLE of $\mu_{x}$ and $\sigma_x^2$. for the conditions of Problem [1, p. 60 exercise 3.4] (see also [2, solution of exercise 3.4]). We are asked if the MLE of the parameters are asymptotically unbiased , efficient and Gaussianly distributed.
In [1, p. 61 exercise 3.6] we are asked to assume that the variance is to be estimated as well as the mean for the conditions of [1, p. 60 exercise 3.4] (see also [2, solution of exercise 3.4]) . We are asked to prove for the vector parameter $\mathbf{\theta}=\left[\mu_x \; \sigma^2_x\right]^T$, that the Fisher information matrix is
 $\mathbf{I}_{\theta}=\left[\begin{array}{cc} \frac{N}{\sigma^2_x} & 0 \\ 0 & \frac{N}{2\sigma^4_x} \end{array}\right]$
Furthermore we are asked to find the CR bound and to determine if the sample mean $\hat{\mu}_x$ is efficient. If additionaly the variance is to be estimated as
 $\hat{\sigma}^2_x=\frac{1}{N-1}\sum\limits_{n=0}^{N-1}(x[n]-\hat{\mu}_x)^2$
 $\frac{(N-1)\hat{\sigma}^2_x}{\sigma^2_x} \sim \chi^2_{N-1}$