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Recently I was trying to combine p-values (e.g. by Fisher's method), but during that investigation I got led astray. I started looking at the distribution of the geometric mean of sets of independent Uniform(0,1) variables. I noticed that as the set size increased, the mean of the geometric mean’s distribution appeared to converge to a number. It appeared to converge to the inverse of Euler's number, 1/exp(1) = 0.3678794.

For set size n, I created 5000 n-vectors containing independent Uniform(0,1) draws. I calculated the geometric mean of each of the 5000 n-vectors. Then I calculated the sample mean of those 5000 geometric means. I did this for a range of set sizes from 1 to 1,000,000. Here are the results for the smallest and largest set sizes.

> head(dfRes)  
iGroupSize      mean  
1          1 0.4856412  
2          2 0.4599790  
3          3 0.4188242  
4          4 0.4110794  
5          6 0.3960357  
6         10 0.3839790  
> tail(dfRes)  
iGroupSize      mean  
26     100000 0.3678654  
27     158489 0.3678691  
28     251189 0.3679015  
29     398107 0.3679053  
30     630957 0.3678569  
31    1000000 0.3678630   

As you can see, for n=1 the mean geometric mean is close to 0.5 as expected, and when n is large (in fact for n>100) it was very close to 1/exp(1).

I think this connection between Uniform(0,1) and exp(1) if true is amazing. I can’t see the connection. Is this result well known? Can anyone prove this result?

Thanking you in advance.

Desmond

  • I think these two questions should be introduced to each other - [link] (http://math.stackexchange.com/questions/997763/geometric-mean-of-reals-between-0-and-1). – Desmond Campbell Feb 09 '17 at 00:24

1 Answers1

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Neat find! I think what's happening is this: You're looking at the average of the geometric mean, which is really just $\mathbb{E}[(X_1X_2...X_n)^{1/n}]$ for $X_1,...,X_n$ independent $U(0,1)$ random variables. This can be directly calculated, since the probability density is just 1: $$\mathbb{E}[(X_1X_2...X_n)^{1/n}]=\int_0^1dx_1...\int_0^1dx_n x_1^{1/n}...x_n^{1/n}$$ $$=\left(\int_0^1x_1^{1/n}dx_1\right)...\left(\int_0^1x_n^{1/n}dx_n\right)$$ $$=\left(\frac{1}{1+\frac{1}{n}}x_1^{1+\frac{1}{n}}\vert_0^1\right)...\left(\frac{1}{1+\frac{1}{n}}x_n^{1+\frac{1}{n}}\vert_0^1\right)$$ $$=\frac{1}{(1+\frac{1}{n})^n}$$ One way to define $e$ is $e=\lim_{n\rightarrow\infty}(1+\frac{1}{n})^n$, so that's a proof that this converges to $1/e$. Whether that answers your question of why this converges to $1/e$, I don't know.

Sam Jaques
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  • Ooh, beat me to it! :-) – Brian Tung Feb 07 '17 at 18:35
  • Lovely! Yes that answers my question. I was a bit thrown by your notation. In the 1st line of your maths, I'm used to all the summations being on the left, and all the dx's being on the right. Turns out I had all the tools to work that out myself, but it looked too daunting. – Desmond Campbell Feb 08 '17 at 12:26