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Analytic convariance constant for normal distributions


New member
May 23, 2012
I've come across a constant that I have found useful in calculations involving normal distributions with sign based peturbations (Cyhelsky skew). However, I haven't been able to calculate it's value analytically, but only experimentally. I was wondering if anyone either knew how to calculate it , or if anyone happens to know a name for it and an analytical expression for it.

Given two normal distribution random variables a,b;
Each has a mean of 0, a standard deviation of 1, and NO covariance between them;
Adding the two variables together will give a result with a standard deviation of (2)**0.5.

I am interested in a very similar problem;
Take only samples of a and b which have the same sign and add them; reject all samples in a and b which have opposite signs; compute the result.

The problem introduces a covariance that is positive, but of an unkown value.
I know experimentally, that the result will have a mean of 0, a standard deviation of ~1.809213(2),
However, I can't figure out the solution analytically.

Is this constant a known value that someone has computed before?
If not, can someone give me some pointers on how to calculate the constant?

I know how to take the integral of z*exp(-0.5*x**2)**2, and then using the area of the bell curve (2*pi)**0.5 to compute the variance or standard deviation of the distribution.
I'm just not sure how to set the problem up for two independent normal distributions that are added with the restrictions I have given...