Suppose $X\_{\max}$ is the maximum in a sequence $X\_1,X\_2,\ldots,X\_n$ where each $X\_i\sim\chi^2\_k$ is an i.i.d. chi-squared random variable with $k$ degrees of freedom.


Since chi squared distribution has an exponential tail, for some fixed number of degrees of freedom $k$ we know that $\lim\_{n\rightarrow\infty}\frac{X\_{\max}}{\ln n}=c$ almost surely, with $c$ being a constant (see Example 3.5.6 on page 176 [here](http://books.google.com/books?id=BXOI2pICfJUC&printsec=frontcover&dq=Modelling%20Extremal%20Events%20for%20Insurance%20and%20Finance#v=onepage&q=Modelling%20Extremal%20Events%20for%20Insurance%20and%20Finance&f=false)). A weaker convergence in probability claim can be made using the convergence of the distribution of appropriately centered $X\_{\max}$ to Gumbel (see summary in Table 3.4.4 on page 156 of the [same reference](http://books.google.com/books?id=BXOI2pICfJUC&printsec=frontcover&dq=Modelling%20Extremal%20Events%20for%20Insurance%20and%20Finance#v=onepage&q=Modelling%20Extremal%20Events%20for%20Insurance%20and%20Finance&f=false)).


However, what happens if both the number of degrees of freedom $k$ also increases, though at a rate that is slower than the increase in the number of random variables in the sequence $n$? For example, let $n=k^2$ (in general, my $n=\omega(k)$). Is there a result similar to the above, with $k\rightarrow\infty$ (which implies that $n\rightarrow\infty$)?


Does convergence of the distribution of $X\_{\max}$ to Gumbel even apply with centering in the above-referenced Table 3.4.4 under these conditions? (the norming constant $d\_n$ is negative when $n=k^2$ is substituted, so I think there is trouble here.)



Asymptotic behavior of max of chi-squared distribution
I am sure this must be written somewhere but since I don't know a reference, let me sketch the computation. I hope there is no error in computation below.


As I commented in your related post, what you are dealing with is the sum of $t=k/2$ exponentials (let's assume $k$ is even, the case of $k$ odd is not really that different). I will also assume that $\log n<<t$ since this seems to be the 
regime you care about; the adaptation to the other cases I leave to you, I did not check the details.


Thus, $X\_1=\sum\_{i=1}^t E\_i$, with $E\_i$ exponential variables. Thus, for $w<1$, 
$P(X\_1>t+w\sqrt{t})$ is of order $1$ while for $w>1$ but $w<<\sqrt{t}$, as long as $t\to\infty$, you have Gaussian tails:
$$P(X\_1>t+w\sqrt{t})\sim c\_1 e^{-c\_2 w^2}/g(t)\,,$$
for some function $g(t)$ that is essentially of polynomial growth and which I have not computed - it is $\sqrt{t}$ for moderately large $w$'s but later it may change.
(For this you need to appeal to known results on precise moderate deviations for sums of independent variables, e.g. as in Petrov's book on sums of independent random variables; there is a big difference in your original question between $t$ fixed and $t$ growing in $n$ in that the tail estimate change from Gaussian to exponential, and this affects the scaling.).
When $w$ becomes of order $\sqrt{t}$ or larger, then you get a different tail estimate, 
but I do not discuss this case in details - basically you get into large deviations regime and beyond.


Now, just follow the derivation of the law of the max: you get, for the range you care about,
$$P(X\_{\max}<t+z)\sim (1-c\_1e^{-c\_2 (z/\sqrt{t})^2}/g(t))^n\sim
e^{-c\_1n e^{-c\_2 (z/\sqrt{t})^2}/g(t)}.$$
Now you choose $z=c\_2'\sqrt{t\log n}-c\_3\log g(t)\sqrt{t/\log n}+z'\sqrt{t/\log n}$. Then,
with appropriate choice of constants,
$$P(X\_\max<t+z)\sim e^{-c\_1e^{-c\_4 z'}}\,,$$
which is Gumbel (note that the centering is $t+c\_2'\sqrt{t\log n}-c\_3 (\log g(t))\sqrt{t/\log n}$).
For this to work, I used that $\log n<<t$, so that $z/\sqrt{t}<<\sqrt{t}$.


