First order statistics (min) of n non-identical but independent normal variates [duplicate]

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While I have seen papers and posts about mean and variance of n i.i.d normal random variables, I am looking for the first order statistics of $n$ (specifically $11$) normal, non-identical (different mean and variances) but independent random variables. Specifically :

1. What distribution would that follow? Can it be approximated to known distributions?
2. What would be the mean and variance?

I am currently using Monte-carlo simulation for finding this, but would like a more direct value to save computational time.

marked as duplicate by Alecos Papadopoulos, Andy, gung♦, Glen_b, AlexisSep 7 '14 at 4:29

• Your title doesn't seem to match the questions in the body of the post. Do you want to know about the minimum, or about the distribution more generally (type, mean & variance, etc)? Are you asking about the distribution of a variable that is a function (eg, the sum) of $n$ variables or the distribution of the mixture of $n$ variables? – gung Sep 7 '14 at 15:41
Let's denote the PDF and CDF of the standard normal distribution as $\phi(x)$ and $\Phi(x)$, and suppose that there are $n$ normally distributed random variables with means and standard deviations of $\{\mu_{i}\}$ and $\{\sigma_{i}\}$. Then, the PDF of first-order statistics should be \begin{equation} p(x) = \sum_{i=1}^{n} \frac{1}{\sigma_{i}}\phi(\tfrac{x-\mu_{i}}{\sigma_{i}})\prod_{\substack{j=1\\j\neq i}}^{n}\Big[1- \Phi(\tfrac{x-\mu_{j}}{\sigma_{j}})\Big]. \end{equation} I can't see any obvious way to further simplify this expression.