Distribution of the maximum of noisy random variables

Let $X_1, \ldots, X_N$ be $N$ hidden random iid variables, all with the same standard distribution, let's say uniform $\mathcal{U}(0, 1)$ or Gaussians $\mathcal{N}(0, 1)$ (probably easiest). I observe $N$ corresponding 'noisy' variables $Z_n = X_n + \mathcal{N}(0, \sigma^2)$. I know how to derive the distribution of $X^* = \max({X_1, \ldots, X_N})$ (and correspondingly $Z^*$ if the $X$'s are Gaussians). What I would like to know is how to compute the distribution (or at least the expectation) of $X_{\mathrm{argmax}_n(Z_n)}$.

Intuitively if $\sigma^2$ is small my observed variables will closely follow the hidden ones, and the distribution will be close to $X^*$, while if it is big, they will be dominated by the noise, and the distribution will be the original one of the $X$'s.

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Here's another very recent and very related question: stats.stackexchange.com/questions/10369/… –  cardinal May 13 '11 at 12:37
For me, is not very clear the distribution you want to compute. The most sensible interpretation would lead to the question linked by cardinal above. –  leonbloy May 13 '11 at 13:58
Yes I think that the question is the same (if the $X$'s are Gaussians) except that I am only interested in the 1st largest value, i.e. the max. –  cdubout May 13 '11 at 14:20
I would like to say that the answers to the question linked above answer my question but I have some trouble with them. I do not understand how the last answer derived the expression for the conditional distribution of $\max(X)$ given $\max(Z)$, and I cannot make those results to agree with those of my simulation... –  cdubout May 13 '11 at 15:49
As if I understood correctly $P(\max(X) = x) = \int_{-\infty}^{+\infty} P(\max(X) = x | \max(Z) = z) P(\max(Z) = z) dz$, with $P(\max(X) = x | \max(Z) = z) = \phi \left( \frac{x - \frac{\sigma^2}{1+\sigma^2} z}{\sqrt{\sigma^2 \left( 1 - \frac{\sigma^2}{1+\sigma^2} \right) }} \right)$, and $P(\max(Z) = z) = N \frac{1}{\sigma_z} \phi \left( \frac{z}{\sigma_z} \right) \Phi \left( \frac{z}{\sigma_z} \right)^{N-1}$ where $\sigma_z = \sqrt{1 + \sigma^2}$ right? –  cdubout May 13 '11 at 16:06

Let's say $Z_n = X_n + Y_n$ with $X_n \sim N(0,1)$, $Y_n \sim N(0,\sigma^2)$, and all $X_n$ and $Y_n$ independent. Let $W = X_{{\rm argmax}_n Z_n} = \sum_n X_n \prod_{j \ne n} I_{Z_n > Z_j}$. Thus $E[W] = N E[X_1 \prod_{j > 1} I_{Z_1 > Z_j}$. Now $Z_n \sim N(0,1+\sigma^2)$. Moreover $X_1$ and $I_{Z_1 > Z_j}$ are conditionally independent given $Z_1$, so $E[W] = N E[E[X_1 | Z_1] \prod_{j > 1} E[I_{Z_1 > Z_j}|Z_1]] = N E[Z_1 \Phi(Z_1/\sqrt{1+\sigma^2})^{N-1}$. This is $N \sqrt{1+\sigma^2} E[Z \Phi(Z)^{N-1}]$ where $Z \sim N(0,1)$.
Ok thank you very much, I think I more or less understood. I particularly did not know about the law of total expectation that you used line 4. Shouldn't line 5 read $\frac{1}{\sqrt{1+\sigma^2}}$ rather than $\sqrt{1+\sigma^2}$? And could I simply write the result as $E[W] = \sigma_z^{-1} E[\max_n X_n]$, with $\sigma_z = \sqrt{1+\sigma^2}$? –  cdubout May 13 '11 at 23:25
$E[X_1|Z_1] = \frac{Z_1}{1+\sigma^2}$ and *not simply $Z_1$* as written above! This is because conditioned on $Z_1$, $X_1$ and $Y_1$ are no longer independent. One can see $X_1,Y_1$ as a multivariate normal distribution with a diagonal covariance, and $X_1,Z_1$ as an affine transformation of $X_1,Y_1$, and then use the formula of conditional distributions of multivariate normal distribution to reach that result. –  cdubout May 16 '11 at 8:57