AxiomOfChoice
AxiomOfChoice
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August 28th, 2014 at 3:52:45 PM permalink
Suppose I choose n random numbers, normally distributed with mean μ and standard deviation σ. I would like to know the expected value of the largest of these numbers. Is there a shortcut to calculate this?
Wizard
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August 28th, 2014 at 4:39:17 PM permalink
Quote: AxiomOfChoice

Suppose I choose n random numbers, normally distributed with mean μ and standard deviation σ. I would like to know the expected value of the largest of these numbers. Is there a shortcut to calculate this?



The mean would be tough. However, the median would be a good estimate and I could give you that.

For example, let:

μ = 0
σ = 1
n = 100

0.5 = p^100, where p is the probability of being under the median number.
0.5^0.01 = p
p = 0.993092495
This translates to a Z value of 2.462037838.

So, with 100 standard normal random variables, there is a 50% chance they are all under 2.462037838. I'd hazard to say the mean maximum is close to that.
"For with much wisdom comes much sorrow." -- Ecclesiastes 1:18 (NIV)
AxiomOfChoice
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August 28th, 2014 at 8:23:04 PM permalink
Interesting approach; thanks.
kubikulann
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September 8th, 2014 at 4:01:35 PM permalink
Quote: Wizard

The mean would be tough. However, the median would be a good estimate and I could give you that.

(...) I'd hazard to say the mean maximum is close to that.

I wouldn't bet on it. Distributions of maxima are strongly skewed. On the other hand, variance is probably small, so from a practical point of view the median should be OK.
Reperiet qui quaesiverit
MangoJ
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September 9th, 2014 at 3:12:12 PM permalink
Quote: AxiomOfChoice

Suppose I choose n random numbers, normally distributed with mean μ and standard deviation σ. I would like to know the expected value of the largest of these numbers. Is there a shortcut to calculate this?



Is µ and σ the same for all n random numbers ? If it is, let's discard µ (a shift in the mean), and you can scale σ to 1.

Then you could look for something like (up to constant factors)

<max(x)> = int dx1 dx2 ... dxn (x1^p + x2^p + ... xn^p)^(1/p) * exp( - (x1^2 + x2^2 + ... xn^2) / 2)

In the limit of p-> infinity the p-th root of (x1^p + x2^p + ... xn^p) becomes the maximum of the x's. Maybe there's some kind of transformation that will still be correct in the p-> infinity limit, which can solve the integral.
AxiomOfChoice
AxiomOfChoice
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September 9th, 2014 at 3:52:53 PM permalink
Note that I'm not looking for the limit as n goes to infinity; I want n fixed.

Simple example:

Suppose we play a game where you pick 4 envelopes. Each envelope has a number, chosen randomly with normal distribution and mean $0 and standard deviation $1. You open all 4 envelopes, choose the one with the largest amount, and get that amount of money (or, have to pay if all 4 numbers are negative)

What is your expectation for this game?
MangoJ
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September 13th, 2014 at 10:34:29 AM permalink
You can have n finite, but you take p to infinity.
BruceZ
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May 29th, 2015 at 12:09:13 AM permalink
Quote: Wizard

Quote: Axiom of Choice

Suppose I choose n random numbers, normally distributed with mean μ and standard

deviation σ. I would like to know the expected value of the largest of these numbers. Is there a shortcut to

calculate this?


The mean would be tough...



The cumulative distribution of the maximum of n independent random variables is the product of their n individual cumulative distributions. For standard normal distributions, the cumulative distribution of the maximum is



where

.

So the pdf of the maximum is



.

The mean maximum is then

.

Note that if the random variables are normally distributed with some u other than 0 or some σ other than 1, we can use the same formula for the average maximum and simply multiply the result by σ and add u.

The following R function will find the average maximum of n normally distributed random variables with some u and σ, and it will plot the pdf of the maximum.



nmax= function(n,u=0,sd=1) {
sds = 10
dx = .01
x = seq(-sds,sds,dx)
f.max = n*pnorm(x)^(n-1)*1/sqrt(2*pi)*exp(-x^2/2)
plot(x,f.max,type='l',xlab='x',ylab='fmax(x)')
u + sd*(sum(x*f.max)*dx)
}

Here my numerical integration simply consists of summing values of the function between -10 and +10 standard
deviations, and spaced 0.01 apart.

For your example:

> nmax(100,0,1)
[1] 2.50759363644168



teddys
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June 5th, 2015 at 7:04:41 PM permalink
Oh, my goodness, those are some fancy fonts!
"Dice, verily, are armed with goads and driving-hooks, deceiving and tormenting, causing grievous woe." -Rig Veda 10.34.4
cosmicac
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June 22nd, 2015 at 12:09:18 PM permalink
Wizard Administrator,
My question is about the free craps practice game.
Has the Wizard published test information for his random number generator?
I know he has stated he is proud of the site.
Thank you in advance for your reply.... Cosmicac
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