Approximate order statistics for normal random variables Are there well known formulas for the order statistics of certain random distributions? Particularly the first and last order statistics of a normal 
random variable, but a more general answer would also be appreciated.
Edit: To clarify, I am looking for approximating formulas that can be more-or-less explicitly evaluated, not the exact integral expression.
For example, I have seen the following two approximations for the first order statistic (ie the minimum) of a normal rv:
$e_{1:n} \geq \mu - \frac{n-1}{\sqrt{2n-1}}\sigma$
and
$e_{1:n} \approx \mu + \Phi^{-1} \left( \frac{1}{n+1} \right)\sigma$
The first of these, for $n=200$, gives approximately $e_{1:200} \geq \mu - 10\sigma$ which seems like a wildly loose bound.
The second gives $e_{1:200} \approx \mu - 2.58\sigma$ whereas a quick Monte Carlo  gives $e_{1:200} \approx \mu - 2.75\sigma$, so it's not a bad approximation but not great either, and more importantly I don't have any intuition about where it comes from.
Any help?
 A: The classic reference is Royston (1982)[1] which has algorithms going beyond explicit formulas. It also quotes a well-known formula by Blom (1958):
$E(r:n) \approx \mu + \Phi^{-1}(\frac{r-\alpha}{n-2\alpha+1})\sigma$ with $\alpha=0.375$. This formula gives a multiplier of -2.73 for $n=200, r=1$.
[1]: Algorithm AS 177: Expected Normal Order Statistics (Exact and Approximate) J. P. Royston. Journal of the Royal Statistical Society. Series C (Applied Statistics) Vol. 31, No. 2 (1982), pp. 161-165
A: $$\newcommand{\Pr}{\mathrm{Pr}}\newcommand{\Beta}{\mathrm{Beta}}\newcommand{\Var}{\mathrm{Var}}$$The distribution of the ith order statistic of any continuous random variable with a PDF is given by the "beta-F" compound distribution.  The intuitive way to think about this distribution, is to consider the ith order statistic in a sample of $N$.  Now in order for the value of the ith order statistic of a random variable $X$ to be equal to $x$ we need 3 conditions:


*

*$i-1$ values below $x$, this has probability $F_{X}(x)$ for each observation, where $F_X(x)=\Pr(X<x)$ is the CDF of the random variable X.

*$N-i$ values above $x$, this has probability $1-F_{X}(x)$

*1 value inside a infinitesimal interval containing $x$, this has probability $f_{X}(x)dx$ where $f_{X}(x)dx=dF_{X}(x)=\Pr(x<X<x+dx)$ is the PDF of the random variable $X$


There are ${N \choose 1}{N-1 \choose i-1}$ ways to make this choice, so we have:
$$f_{i}(x_{i})=\frac{N!}{(i-1)!(N-i)!}f_{X}(x_{i})\left[1-F_{X}(x_{i})\right]^{N-i}\left[F_{X}(x_{i})\right]^{i-1}dx$$
EDIT in my original post, I made a very poor attempt at going further from this point, and the comments below reflect this.  I have sought to rectify this below
If we take the mean value of this pdf we get:
$$E(X_{i})=\int_{-\infty}^{\infty} x_{i}f_{i}(x_{i})dx_{i}$$
And in this integral, we make the following change of variable $p_{i}=F_{X}(x_{i})$ (taking @henry's hint), and the integral becomes:
$$E(X_{i})=\int_{0}^{1} F_{X}^{-1}(p_{i})\Beta(p_{i}|i,N-i+1)dp_{i}=E_{\Beta(p_{i}|i,N-i+1)}\left[F_{X}^{-1}(p_{i})\right]$$
So this is the expected value of the inverse CDF, which can be well approximated using the delta method to give:
$$E_{\Beta(p_{i}|i,N-i+1)}\left[F_{X}^{-1}(p_{i})\right]\approx F_{X}^{-1}\left[E_{\Beta(p_{i}|i,N-i+1)}\right]=F_{X}^{-1}\left[\frac{i}{N+1}\right]$$
To make a better approximation, we can expand to 2nd order (prime denoting differentiation), and noting that the second derivative of an inverse is:
$$\frac{\partial^{2}}{\partial a^{2}}F_{X}^{-1}(a)=-\frac{F_{X}^{''}(F_{X}^{-1}(a))}{\left[F_{X}^{'}(F_{X}^{-1}(a))\right]^{3}}=-\frac{f_{X}^{'}(F_{X}^{-1}(a))}{\left[f_{X}(F_{X}^{-1}(a))\right]^{3}}$$
Let $\nu_{i}=F_{X}^{-1}\left[\frac{i}{N+1}\right]$.  Then We have:
$$E_{\Beta(p_{i}|i,N-i+1)}\left[F_{X}^{-1}(p_{i})\right]\approx F_{X}^{-1}\left[\nu_{i}\right]-\frac{\Var_{\Beta(p_{i}|i,N-i+1)}\left[p_{i}\right]}{2}\frac{f_{X}^{'}(\nu_{i})}{\left[f_{X}(\nu_{i})\right]^{3}}$$
$$=\nu_{i}-\frac{\left(\frac{i}{N+1}\right)\left(1-\frac{i}{N+1}\right)}{2(N+2)}\frac{f_{X}^{'}(\nu_{i})}{\left[f_{X}(\nu_{i})\right]^{3}}$$
Now, specialising to normal case we have
$$f_{X}(x)=\frac{1}{\sigma}\phi(\frac{x-\mu}{\sigma})\rightarrow f_{X}^{'}(x)=-\frac{x-\mu}{\sigma^{3}}\phi(\frac{x-\mu}{\sigma})=-\frac{x-\mu}{\sigma^{2}}f_{X}(x)$$
$$F_{X}(x)=\Phi(\frac{x-\mu}{\sigma})\implies F_{X}^{-1}(x)=\mu+\sigma\Phi^{-1}(x)$$
Note that $f_{X}(\nu_{i})=\frac{1}{\sigma}\phi\left[\Phi^{-1}\left(\frac{i}{N+1}\right)\right]$ And the expectation approximately becomes:
$$E[x_{i}]\approx \mu+\sigma\Phi^{-1}\left(\frac{i}{N+1}\right)+\frac{\left(\frac{i}{N+1}\right)\left(1-\frac{i}{N+1}\right)}{2(N+2)}\frac{\sigma\Phi^{-1}\left(\frac{i}{N+1}\right)}{\left[\phi\left[\Phi^{-1}\left(\frac{i}{N+1}\right)\right]\right]^{2}}$$
And finally:
$$E[x_{i}]\approx \mu+\sigma\Phi^{-1}\left(\frac{i}{N+1}\right)\left[1+\frac{\left(\frac{i}{N+1}\right)\left(1-\frac{i}{N+1}\right)}{2(N+2)\left[\phi\left[\Phi^{-1}\left(\frac{i}{N+1}\right)\right]\right]^{2}}\right]$$
Although as @whuber has noted, this will not be accurate in the tails.  In fact I think it may be worse, because of the skewness of a beta with different parameters
A: Aniko's answer relies on Blom's well known formula that involves a choice of $\alpha = 3/8$. It turns out that this formula is itself a mere approximation of an exact answer due to G. Elfving (1947), The asymptotical distribution of range in samples from a normal population, Biometrika, Vol. 34, pp. 111-119. Elfving's formula is aimed at the minimum and maximum of the sample, for which the correct choice of alpha is $\pi/8$. Blom's formula results when we approximate $\pi$ by $3$.
By using the Elfving formula rather than Blom's approximation, we get a multiplier of -2.744165. This number is closer to Erik P.'s exact answer (-2.746) and to the Monte Carlo approximation (-2.75) than is Blom's approximation (-2.73), while being easier to implement than the exact formula.
A: Depending on what you want to do, this answer may or may not help - I got the following exact formula from Maple's Statistics package.
with(Statistics):
X := OrderStatistic(Normal(0, 1), 1, n):
m := Mean(X):
m;

$$\int _{-\infty }^{\infty }\!1/2\,{\frac {{\it \_t0}\,n!\,\sqrt {2}{
{\rm e}^{-1/2\,{{\it \_t0}}^{2}}} \left( 1/2-1/2\,
{{\rm erf}\left(1/2\,{\it \_t0}\,\sqrt {2}\right)} \right) ^{-1+n}}{
 \left( -1+n \right) !\,\sqrt {\pi }}}{d{\it \_t0}}$$
By itself this isn't very useful (and it could probably be derived fairly easily by hand, since it's the minimum of $n$ random variables), but it does allow for quick and very accurate approximation for given values of $n $ - much more accurate than Monte Carlo:
evalf(eval(m, n = 200));
evalf[25](eval(m, n = 200));

gives -2.746042447 and -2.746042447451154492412344, respectively.
(Full disclosure - I maintain this package.)
