Consider the following process for generating a random sample:

  • Sample $X_1, X_2, \dots, X_n \sim \mathcal{N}(0,1)$
  • Compute $M = \max\limits_i |X_i|$
  • Scale the values to get $Z_i = X_i / M$

Can we say anything about the convergence of distribution of $Z_i$ as $n\to\infty$?

The $Z_i$s are clearly identically distributed, but they aren't independent since for any $n$ almost certainly exactly 1 $Z_i$ will take the value -1 or 1.

Here's what the points are distributed like for various values of $n$ (65,000 trials each):

$n=16$ enter image description here

$n=64$ enter image description here

$n=4,096$ enter image description here

These all use 100 evenly spaced bins, hence the spikes at the ends for the smaller values of $n$. Does the limiting distribution have a name?

Edit: I should clarify that the obvious candidate thing for it to converge to would be a point mass on 0, but I'm not sure whether it does that or not.

Renamed $Y$ to $Z$ for consistency with the plots.

  • $\begingroup$ If the cdf for $Y_n$ is $F_n$, and the cdf of the standard normal is $\Phi$, then I would expect $F_n(x E[M_n]) \to \Phi(x)$. $\endgroup$
    – Matt F.
    May 24, 2023 at 1:45
  • $\begingroup$ I don't think the support works out there does it? $\endgroup$ May 24, 2023 at 1:54
  • $\begingroup$ This q&a may be helpful: math.stackexchange.com/questions/89030/…. $\endgroup$
    – jbowman
    May 24, 2023 at 2:22
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    $\begingroup$ For some $N$, $n > N \implies E[M_n] > 1$, at which point $n > N \implies F_n(-2 E[M_n]) = 0$, but $\Phi(-2) \neq 0$ $\endgroup$ May 24, 2023 at 3:45
  • 1
    $\begingroup$ Maybe add the tag "extreme-value"? $\endgroup$
    – Yves
    May 24, 2023 at 12:34

2 Answers 2


$Z$ converges to point mass at zero. We know $M_n\stackrel{p}{\to}\infty$ since the Normal distribution is unbounded.

So, for any fixed $\epsilon$ and any $i$, $$P(|Z_i|>\epsilon)=P(|X_i/M_n|>\epsilon)\to 0$$ implying $Z_i\stackrel{p}{\to} 0$ and $Z_i\stackrel{d}{\to} 0$.

The convergence will be quite slow, because $M_n$ increases roughly as $\sqrt{\log n}$. It's going to be relatively hard to get convincing simulations for the Gaussian case.

  • $\begingroup$ Usually this kind of convergence statement is of little use, as in this case, because the sequence, properly normalized, does converge to a Normal distribution. $\endgroup$
    – whuber
    May 25, 2023 at 10:57
  • $\begingroup$ This is actually for a practical application, in which the choice of normalizing is not available: arxiv.org/abs/2305.14314 (There's a lot of stuff going on in that paper, only the NF4 data type stuff is relevant here) $\endgroup$ May 25, 2023 at 18:14
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    $\begingroup$ That's a fascinating paper, thank you. But this sounds rather like an X/Y problem: if the issue is with low-precision floating point computation, the question you asked scarcely looks relevant. $\endgroup$
    – whuber
    May 25, 2023 at 19:42
  • $\begingroup$ It's relevant because the way the quantization algorithm works is to take a batch of values, then divide them by the max absolute value to scale them into [-1, 1]. The authors propose a discretization of [-1, 1] based on quantiles of the normal distribution. The reason for my question is that I was wondering whether there was some known distribution the quantiles should be calculated from instead. $\endgroup$ May 25, 2023 at 21:18
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    $\begingroup$ That doesn't sound like "the choice of normalizing is not available"! $\endgroup$
    – whuber
    May 26, 2023 at 14:42

The following demonstration is not rigorous, but it's correct :-).

Because the Normal distribution is continuous, there is almost surely a unique maximum absolute value. Thus, the chance that $|Z_n|=1$ is $1/n.$ In all other cases $Z_n = X_n/X_i$ where $X_i$ attains the maximum absolute value. Furthermore, for extremely large $n,$ the standard deviation of the distribution of that maximum $M_n$ shrinks towards zero as $n$ grows. Consequently, $M_n$ is nearly constant and will necessarily be close to its median.

Because the CDF of the maximum is $\Phi(x)^n,$ its median $m_n$ is where $\Phi(m_n)^n = 1/2,$ so that

$$M_n \approx m_n = \Phi^{-1}(2^{-1/n}).$$


$Z_n$ will (with probability $1-1/n$) be arbitrarily close to $X_n/m_n,$ which has a mean-zero Normal distribution with standard deviation $1/m_n.$

In a simulation of $5\,000$ samples, each of size $50\,000,$ I computed all the $Z_n.$ Here are histograms for four selected values of $n.$ Over them I have plotted the Normal approximation. The p-value is that obtained by removing the values of $Z_n$ equal to $1$ (we know they contribute a small "atom") and applying a Kolmogorov-Smirnov test to the rest.

enter image description here

Even with this $5\,000$ samples, the KS test -- which is notorious for its power to distinguish Normal from non-Normal data (some people on this site have claimed it will always reject Normality on any dataset this large) -- does not reject the foregoing claim when $n = 50\,000.$

More specifically, upon dividing $Z_n$ by the standard deviation $1/m_n,$ we find the distribution of $Z_n \Phi^{-1}(2^{-1/n})$ converges to the standard Normal distribution. An immediate consequence is that the distribution of $Z_n$ converges to zero at a rate given by $1 / \Phi^{-1}(2^{-1/n}).$ This is a slow rate, because a crude approximation gives $m_n = O(1/\sqrt{\log n}).$ For instance, $1/m_n=1/10$ for $n \approx e^{100} \approx 10^{43}$ and a standard deviation of $1/10$ is still appreciable. Thus, in any application it would usually be unwise to approximate $Z_n$ by $0:$ use this limiting Normal distribution instead as a better approximation.

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    $\begingroup$ Thanks for the analysis! I'll try this out as a quantization grid and see how it works. $\endgroup$ May 26, 2023 at 21:07
  • $\begingroup$ I think that should be $Z_n \times \Phi^-1(2^{-1/n})$ right? If you divide by a quantity which is growing in $n$ it'll still converge to 0. $\endgroup$ May 26, 2023 at 21:56
  • $\begingroup$ Of course--thanks for the correction. I see another typo to fix, too... $\endgroup$
    – whuber
    May 26, 2023 at 21:59
  • $\begingroup$ There's one other issue, which is that rather than $\Phi^{-1}$, one should use the inverse CDF of the half-normal distribution (since $M$ is the max absolute value), but that's easy to implement. $\endgroup$ May 26, 2023 at 22:05
  • $\begingroup$ Actually, that's not needed -- but it's a great suggestion and would be better insofar as it more clearly indicates the role played by this quantity. $\endgroup$
    – whuber
    May 26, 2023 at 22:11

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