Maximum of dependent exponential random variables

In summary, p(z_{\max }) is the PDF of the maximum of a set of 4 chi-square random variables. The PDF is dependent and can be calculated by dividing the space of z into four parts and integrating over each part.
  • #1
chased
2
0
Pdf (or mgf) of maximum of dependent exponential random variables ?

max of Z1, Z2, Z3, Z4

where

Z1 = |X1+X2+X3|^2 + |Y1+Y2+Y3|^2
Z2 = |X1-X2+X3|^2 + |Y1-Y2+Y3|^2
Z3 = |X1+X2-X3|^2 + |Y1+Y2-Y3|^2
Z4 = |X1-X2-X3|^2 + |Y1-Y2-Y3|^2

Xi, Yi are independent zero mean normal with variance 1/2.

So, Z1,Z2,Z3,Z4 are indentically distributed exponential random variables,
But they are correlated.

Can anybody help me?
 
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  • #2
Sorry are the Xis and Yis independent of each other or are they all independent of everything else?

If they are all independent of each other then you have a Chi square as the sums of normals are a normal, and square of a normal is chi-squared and sums of chi-squares are chi-squared.

Hope that's helpful
 
  • #3
Thank you. Xis and Yis are all independent of each other.
Yes. I have 4 Chi-square RVs. They are dependent.
Can I find the pdf of the maximum of those?
 
  • #4
Here is a symbolic description of the solution to the problem, hope it makes
any sense:You want to calculate the PDF of a variable [tex]Z_{\max } = \max \left( {Z_1
,Z_2 ,Z_3 ,Z_4 } \right)[/tex] where
[tex]
\begin{array}{l}
Z_1 = f_1 ({\rm {\bf X,Y}}) = \left| {X_1 + X_2 + X_3 } \right|^2 + \left|
{Y_1 + Y_2 + Y_3 } \right|^2 \\
Z_2 = f_2 ({\rm {\bf X,Y}}) = \left| {X_1 - X_2 + X_3 } \right|^2 + \left|
{Y_1 - Y_2 + Y_3 } \right|^2 \\
Z_3 = f_3 ({\rm {\bf X,Y}}) = \left| {X_1 + X_2 - X_3 } \right|^2 + \left|
{Y_1 + Y_2 - Y_3 } \right|^2 \\
Z_4 = f_4 ({\rm {\bf X,Y}}) = \left| {X_1 - X_2 - X_3 } \right|^2 + \left|
{Y_1 - Y_2 - Y_3 } \right|^2 \\
\end{array}
[/tex]You can write a symbolic solution to the PDF of any function [tex]Z=f(X)[/tex] of a stochastic variable [tex]X[/tex] as
[tex]
p(z) = \int {p({ {x}})\delta \left( {z - f \left(
{x} \right)} \right)d{ {x}}}
[/tex]
where [tex]\delta(z)[/tex] is Dirac's delta function.

Thus, the PDF of [tex]Z_{\max } = \max \left(
{Z_1 ,Z_2 ,Z_3 ,Z_4 } \right)[/tex] can be written as


[tex]
p(z_{\max } ) = \int {p({\rm {\bf z}})\delta \left( {z_{\max } - \max \left(
{z_1 ,z_2 ,z_3 ,z_4 } \right)} \right)d{\rm {\bf z}}} \qquad , \qquad (1)
[/tex]
where
[tex]
p({\rm {\bf z}}) = \int {\int {p({\rm {\bf x}},{\rm {\bf y}})\prod\limits_{k
= 1}^4 {\delta \left( {z_k - f_k ({\rm {\bf x}},{\rm {\bf y}})} \right)}
d{\rm {\bf x}}} d{\rm {\bf y}}}
[/tex]

[tex]
p({\rm {\bf x}},{\rm {\bf y}}) = p(x_1 )p(x_2 )p(x_3 )p(y_1 )p(y_2 )p(y_3 )
[/tex]

and integration with respect to a vector
[tex]\int { \cdot d{\rm {\bf x}}} [/tex]
stands for integration over all components:
[tex]\int\limits_{ - \infty }^\infty
{\int\limits_{ - \infty }^\infty {\int\limits_{ - \infty }^\infty { \cdot
dx_1 dx_2 dx_3 } } } [/tex]

If you had already calculated [tex]p({\rm {\bf z}})[/tex], the integral in (1) can be
calculated by dividing the four-dimensional space of [tex]{\rm {\bf z}}[/tex] into
four parts, over each of which [tex]\max \left( {z_1 ,z_2 ,z_3 ,z_4 } \right)[/tex]
is linear. The integral them becomes a sum of four parts, albeit with
slightly complicated bounds.

The resulting expression is likely to be quite messy, I'd recommend using an
analytic math program (e.g. Matematica or Maple) to compute it.
 

1. What is the definition of maximum of dependent exponential random variables?

The maximum of dependent exponential random variables is a statistical concept that refers to the largest value that can be obtained from a group of variables that are dependent on each other and follow an exponential distribution. It is also known as the maximum of correlated exponential random variables.

2. How is the maximum of dependent exponential random variables calculated?

The maximum of dependent exponential random variables can be calculated by finding the joint distribution of the variables and then taking the maximum value from that distribution. This can be done using mathematical formulas or through simulation methods.

3. What are some real-life applications of the maximum of dependent exponential random variables?

The maximum of dependent exponential random variables has various applications in fields such as engineering, finance, and biology. For example, it can be used to model the time until failure of a system with multiple components, the time until default of a portfolio of loans, or the time between mutations in a genetic sequence.

4. How does the maximum of dependent exponential random variables differ from the maximum of independent exponential random variables?

The main difference between the maximum of dependent and independent exponential random variables is that in the dependent case, the variables are correlated, meaning that the value of one variable can affect the value of another. In the independent case, the variables are not correlated and can be treated as separate and unrelated.

5. Are there any limitations or assumptions associated with the maximum of dependent exponential random variables?

As with any statistical model, there are limitations and assumptions associated with the maximum of dependent exponential random variables. One major assumption is that the variables follow an exponential distribution, which may not always be the case in real-world situations. Additionally, the model may not be appropriate for certain types of data, such as discrete or continuous data that does not follow an exponential distribution.

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