Maximum of two correlated random variables

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SUMMARY

The discussion centers on finding the maximum of two correlated non-Gaussian random variables, A and B. The user seeks an analytical or approximate solution to their bivariate distribution, referencing Clark's work on Gaussian variables. The proposed method involves using a quadratic Taylor polynomial to approximate the maximum operation, specifically through the expression Max(A,B) = (A + B + abs(A - B)) / 2. The user is looking for ways to analytically express abs(A - B) without regression, given the coefficients of A and B based on correlated parameters x and y.

PREREQUISITES
  • Understanding of bivariate distributions and joint probability density functions (JPDF)
  • Familiarity with quadratic Taylor polynomials and their applications
  • Knowledge of non-Gaussian random variables and their properties
  • Basic statistical concepts related to correlation and maximum operations
NEXT STEPS
  • Research methods for deriving joint probability density functions for correlated non-Gaussian variables
  • Explore advanced techniques in approximation theory, particularly for absolute differences
  • Study the implications of Clark's work on non-Gaussian distributions
  • Investigate numerical methods for estimating maximum operations in random variables
USEFUL FOR

Statisticians, data scientists, and researchers working with correlated random variables, particularly in fields requiring non-Gaussian analysis and approximation techniques.

touqeerazam
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Hi all,

I want to find maximum of two random variables which are correlated and are non gaussian too. Baiscally I need an analytical orr approximate solution to their bivaraite distribution with means and varaince of resulting distribution. There is some work by Clark 'The greatest of finite set of random variables' but that assumes gaussian correlated variables.

so if A & B are two correlated random varaibles. I need C=Max(A,B)?

one other method is to use quadratic taylor polynomial for A & B. and use Max (A,B)=(A+B+abs(A-B))/2. But I don't know can i approximate abs(A-B) by quadratic polynomial (without regression). In other words, if I can get any method to approximate abs(A-B) by analytical expression. This will also give me Max operation (what I really need).

Sorry for long question

I will be very grateful to you if anyone could figure out solution or any directions

cheers
Touqeer
 
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Are you given the joint distribution f of Aand B? then you could try and compute the distribution of C=max(A,B) as

<br /> \mathbb{P}(C\leq c)=\int_{-\infty}^c{da\,\int_{-\infty}^a{db\,f(a,b)}}+\int_{-\infty}^c{db\,\int_{-\infty}^b{da\,f(a,b)}}.<br />
 
Hi,

Thanks Pere. I don't have their joint dis 'f'. Its difficult to get JPDF of correlated non gaussian variables (not sure how to get). What all I have is varaible A & B given as,

A=a0+a1x+a2x^2+a3y+a4y^2
B=b0+b1x+b2x^2+b3y+b4y^2

where x & y are two parameters which are correlated non gaussian, and I have their PDFs. ai's and bi's are just coefficients.

Can you please point out any solution?

Thanks
Touqeer
 

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