Distribution of the sum of three random variables

In summary, the problem is to find the probability density function of a random variable U, which is defined as the sum of three independent random variables X, Y, Z divided by three. Two methods are suggested: using the cumulative distribution function or using the expected value of h(U). Both methods involve changing the order of integration and setting up a new integral to find the density function.
  • #1
TeXfreak
1
0
Hi everyone. I have this problem. Given three random variables X, Y, Z with joint pdf (probability density function)

f(x,y,z)=\exp(-(x+y+z)) if x>0, y>0, z>0; 0 elsewhere

find the pdf of U (f_U), where U is the random variable given by U=(X+Y+Z)/3.

Now I know how to find the joint pdf of a random vector of equal dimension as that of the original vector (via the Jacobian of the inverse transformation, that is, when the transformation is from R^n to R^n, but in this case it is from R^3 to R), or how to find the pdf of the sum of two independent random variables (via the convolution of the two pdfs), but I can't figure out how to do this one.

One could set the transformation to be g : R^3 \to R^3 defined by g(x,y,z)=((x+y+z)/3,y,z) (though I am not sure whether that would be right), so as to find the pdf of g(X,Y,Z) and then find the marginal density function of U, but then the integral does not converge.

And trying convolutions, something like f_U = f_X * (f_Y * f_Z) ---and here I am less sure if it's ok--- the integral doesn't converge either.

Could anybody can help me with this problem, please? Thanks in advance.
 
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  • #2
First find the cumulative distribution function, F_U(u) = P((X+Y+Z)/3 < u), by integrating the joint density function f(x,y,z) over the tetrahedron satisfying x > 0, y > 0, z > 0, (x+y+z)/3 < u. Then differentiate F_U to get the density function for U.
 
  • #3
A general method I often use is as follows. The probability density function fU of a random variable U is defined by the following expression for the expected value of h(U), for any function h

[tex]
E[h(U)] = \int f_U(u) h(u)\,du.
[/tex]

Just substitute in U=(X+Y+Z)/3

[tex]
\int f_U(u) h(u)\,du=E[h((X+Y+Z)/3)]=\int_0^\infty \int_0^\infty\int_0^\infty f(x,y,z)h((x+y+z)/3) \,dx\,dy\,dz
[/tex]

change variables x = 3u - y - z in the inner integral

[tex]
\int f_U(u) h(u)\,du=\int_0^\infty \int_0^\infty\int_{(y+z)/3}^\infty f(3u-y-z,y,z)h(u) 3\,du\,dy\,dz
[/tex]

Change the order of integration

[tex]
\int f_U(u) h(u)\,du=\int_0^\infty\int_0^{3u} \int_0^{3u-z} f(3u-y-z,y,z)h(u) 3\,dy\,dz\,du
[/tex]

from which you can read off the density

[tex]
f_U(u)=3\int_0^{3u} \int_0^{3u-z} f(3u-y-z,y,z) \,dy\,dz
[/tex]

This is virtually the same as calculating the cumulative distribution (by taking [itex]g(u)=1_{\{u>K\}}[/itex]), but without the differentiation step to convert to the density at the end. So, whichever method you prefer.
 

Related to Distribution of the sum of three random variables

1. What is the distribution of the sum of three random variables?

The distribution of the sum of three random variables is a probability distribution that describes the likelihood of different outcomes when three random variables are added together. It is a combination of the individual distributions of each variable and can take on various forms, such as normal, binomial, or Poisson.

2. How do you calculate the distribution of the sum of three random variables?

To calculate the distribution of the sum of three random variables, you need to know the individual distributions of each variable and their corresponding probabilities. You can then use mathematical formulas or statistical software to combine these distributions and determine the overall distribution of the sum.

3. What assumptions are made when calculating the distribution of the sum of three random variables?

The assumptions made when calculating the distribution of the sum of three random variables include the independence of the variables, the linearity of the sum, and the normality of the individual distributions. These assumptions may vary depending on the specific variables and their distributions.

4. Can the distribution of the sum of three random variables be approximated by a normal distribution?

In some cases, the distribution of the sum of three random variables can be approximated by a normal distribution, especially if the individual distributions are also normal. This is known as the Central Limit Theorem and is a common approximation used in statistical analysis.

5. How is the distribution of the sum of three random variables used in practical applications?

The distribution of the sum of three random variables has many practical applications, such as in finance, engineering, and social sciences. It can be used to model and predict outcomes in complex systems, evaluate risk, and make decisions based on statistical analysis.

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