 Solution

For large $t$, write the survival function asA positive function $\ell$ is slowly varying if $\ell (tu)/\ell (t)\to 1$ for every fixed $u>0$. Heresince $\log (tu)=\log t+\log u$ and $\log \log (tu)-\log \log t\to 0$. Thus $\overline{F}$ has regular variation of index $-2$.

The relevant extreme-value criterion is that an infinite right endpoint and a survival function regularly varying with index $-r$, $r>0$, give attraction to the standard Fréchet distribution of shape $r$. Its distribution function is $G_r(x)=e^{-x^{-r}}$ for $x>0$ and zero for $x\le 0$. Here is a direct verification, so no additional theorem is needed to establish the criterion in this example. Choose $a_n\to \infty$ satisfying $n\overline{F}(a_n)\to 1$ and set $b_n=0$. For $x>0$,If $u_n\to 0$ and $n{u}_n\to c$, then $n\log (1-u_n)=-n{u}_n+O(n{u}_n^2)\to -c$. Applying this with $u_n=\overline{F}(a_{n}x)$ gives $F(a_{n}x{)}^n\to e^{-x^{-2}}$. For $x\le 0$, the distribution function is zero because the observations are supported on $[10,\infty )$. ThereforeFor example, elementary asymptotic normalizers arefor sufficiently large $n$, with arbitrary positive definitions at smaller indices. Indeed $\log a_n\sim \frac{1}{2}\log n$ and $\log \log a_n\sim \log \log n$, so $a_n^2\log a_n\log \log a_n\sim Cn$. The precise valid choice of $C$ at the lower endpoint does not change the limiting shape.