 Solution

For the normal distribution, the density at its centre is $f(\theta )=1/(\sigma \sqrt{2\pi })$. The sample mean has exact variance ${\sigma }^2/n$, whereas part (b) gives the sample median the leading variance $\pi {\sigma }^2/(2n)$. Thus the requested asymptotic relative efficiency isAt a fixed confidence level, a confidence interval based on the sample mean has a leading half-width smaller by the factor $\sqrt{2/\pi }$. With known $\sigma$, $\overline{X}\pm z_{1-\alpha /2}\sigma /\sqrt{n}$ also has exact normal coverage. Prefer the sample mean under the normal model.

For the Laplace distribution, symmetry puts both the mean and median at $\theta$. Its density there is $1/(2\sigma )$, andConsequently the sample mean has variance $2{\sigma }^2/n$, while the sample median has leading variance ${\sigma }^2/n$. HenceA large-sample confidence interval based on the sample median has leading half-width smaller by $1/\sqrt{2}$; with known scale it is $M_X\pm z_{1-\alpha /2}\sigma /\sqrt{n}$. Prefer the sample median under the Laplace model. Consistent estimation of an unknown scale preserves these leading comparisons. The reason for the reversal is quantitative: the median's precision depends on the central density $f(\theta )$, while the sample mean's precision depends on the whole population variance.