Proving Sum of 2 Indep. Cauchy RVs is Cauchy

In summary, the problem is to show that the sum of two independent Cauchy random variables is also a Cauchy random variable. The given hint suggests using the convolution integral to solve the problem, but the speaker is unable to prove its validity. An alternate approach using characteristic functions is mentioned as a potential solution.
  • #1
glacier302
35
0
Given the fact that X and Y are independent Cauchy random variables, I want to show that Z = X+Y is also a Cauchy random variable.

I am given that X and Y are independent and identically distributed (both Cauchy), with density function
f(x) = 1/(∏(1+x2)) . I also use the fact the convolution integral for X and Y is ∫f(x)f(y-x)dx .

My book says to use the following hint:

f(x)f(y-x) = (f(x)+f(y-x))/(∏(4+y2)) + 2/(∏y(4+y2))(xf(x)+(y-x)f(y-x)) .

Using this hint, I'm able to solve the rest of the problem, but I can't figure out how to prove that this hint is true.

Any help would be much appreciated : )
 
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  • #2


I can't see what to do with the hint. There is an alternate approach which might be easier if you have been exposed to characteristic functions (Fourier transform of densities).
If you have, then get the characteristic function of Cauchy, square it and then get the inverse transform. This is the density that you want.
 
  • #3


I notice than in several statistics texts, this result is asserted or assigned as an exercise and the indicated method is expand the integrand in partial fractions. Perhaps the identity would be the result of that.
 

FAQ: Proving Sum of 2 Indep. Cauchy RVs is Cauchy

1. What is the Cauchy distribution?

The Cauchy distribution is a continuous probability distribution that is often used to model extreme events or outliers in data. It is characterized by its heavy tails, which means that it has a higher probability of extreme values compared to other distributions.

2. What does it mean for two random variables to be independent?

Two random variables are considered independent if the outcome of one does not affect the outcome of the other. This means that the probability of one variable occurring does not change based on the outcome of the other variable.

3. How do you prove that the sum of two independent Cauchy random variables is also Cauchy?

This can be proven using the characteristic function of the Cauchy distribution. By taking the product of the characteristic functions of the two independent Cauchy random variables, we can show that the resulting characteristic function is also that of a Cauchy distribution, therefore proving that the sum of the two variables is also Cauchy.

4. What is the significance of proving the sum of two independent Cauchy random variables is Cauchy?

Proving this property is important because it allows us to better understand and analyze data that follows a Cauchy distribution. By knowing that the sum of two independent Cauchy random variables is also Cauchy, we can make more accurate predictions and draw conclusions about the data.

5. Are there any limitations to this proof?

Yes, the proof assumes that the two Cauchy random variables are independent. In real world scenarios, this may not always be the case, and therefore the result may not hold. Additionally, the proof only applies to the sum of two Cauchy random variables and may not be generalizable to the sum of more than two variables.

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