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Given a random vector

\begin{equation} x=(x_1, \ldots, x_n) \end{equation}

with independent and identically distributed entries $x_i \sim \mathcal{N}(0,\sigma^2)$, I would like to find a lower bound $f(n)$

\begin{equation} \mathbb{E}[\|x\|^2_{\infty}] \geq f(n) \end{equation}

which is reasonably tight. I know that the following equality for the non squared norm holds when $\sigma^2 =1$:

\begin{equation} E(\|x\|_\infty)=\int_0^\infty(1-(2\Phi(x)-1)^n)dx, \end{equation}

where $\Phi$ is the CDF of $\mathcal{N}(0,1)$, see the comment to this question by @Did here. Unfortunately I am not even sure on how to (tightly) lower bound the right integral for this special case.

Any help on solving the general case is much appreciated.

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  • $\begingroup$ what is $\Phi$? the CDF of a $\mathcal N(0, 1)$? And $k$? Should it be $n$? $\endgroup$
    – G. Gare
    Nov 5 '19 at 9:41
  • $\begingroup$ yes, and yes, I will add that, thank you. $\endgroup$
    – sigmatau
    Nov 5 '19 at 9:45
  • $\begingroup$ By the way, the equality you give is only valid for $x_i \sim \mathcal N(0, 1)$, not for $x_i \sim \mathcal N(0, \sigma^2)$ as you claim. You can do some math and get an equality for $\mathcal N(0, \sigma^2)$ or restate the question. $\endgroup$
    – G. Gare
    Nov 5 '19 at 9:51
  • $\begingroup$ edited thank you. $\endgroup$
    – sigmatau
    Nov 5 '19 at 9:52
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    $\begingroup$ Here is the exact formula: $$ \mathsf{E}\|X\|_{\infty}^2=2\sigma^2\int_0^{\infty}x(1-(2\Phi(x)-1)^n)\,dx. $$ $\endgroup$
    – d.k.o.
    Nov 6 '19 at 22:04
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Let $M_n:=\max_{1\le i\le n}|X_i|$. Assume w.l.o.g. that $\sigma=1$. Then, using Markov's inequality ($n\ge 2$), for any $c>0$, \begin{align} \mathsf{E}M_n^2&\ge c\ln n\times\mathsf{P}(M_n\ge \sqrt{c\ln n}) \\[0.5em] &=c\ln n\times (1-[\mathsf{P}(|X_1|<\sqrt{c\ln n})]^n). \end{align}

Using the bound on the error function: $\operatorname{erf}(x)\le \sqrt{1-\exp(-4x^2/\pi)}$, one gets \begin{align} \mathsf{P}(|X_1|< \sqrt{c\ln n})&=\operatorname{erf}\left(\sqrt{c\ln(n)/2}\right) \\ &\le \sqrt{1-\exp(-2c\ln(n)/\pi)} \\ &=\sqrt{1-n^{-2c/\pi}}. \end{align}

Thus, for $c\in (0,\pi/2)$, $$ \mathsf{E}M_n^2\ge c\ln n\times\left(1-\left(1-n^{-2c/\pi}\right)^{n/2}\right)=c\ln n\times(1-o(1)). $$

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