Distribution of two independent exponential random variables

In summary: Your final answer looks correct. In summary, to find the distribution of Z = X_1 / X_2, we can approach it by finding the cumulative distribution function for Z and differentiating it. Using this method, we arrive at the final distribution function of Z, which is given by f_Z(a) = \frac{\lambda_1 \lambda_2}{(\lambda_1 a + \lambda_2)^2}. This method seems to be a valid approach for solving this question.
  • #1
lizzyb
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Q: If [tex]X_1[/tex] and [tex]X_2[/tex] are independent exponential random variables with respective parameters [tex]\lambda_1[/tex] and [tex]\lambda_2[/tex], find the distribution of [tex]Z = X_1 / X_2[/tex].

Discussion

The best method to attack this problem apparent to me is coming up with a cumulative distributive function for [tex]Z[/tex] and then differentiating it.

Work Completed So Far
(note: I don't mean for anyone to check if I did my math right I'm just wondering if I'm going about solving the question right)

[tex]F_Z (a) = P\{ Z \leq a \} = P\{ X_1 / X_2 \leq a \} = P \{ X_1 \leq a X_2 \}[/tex]
[tex]\int_0^\infty \int_0^{a x_2} \lambda_1 e^{- \lambda_1 x_1} dx_1 \lambda_2 e^{- \lambda_2 x_2} dx_2 = \lambda_1 \lambda_2 \int_0^\infty e^{\lambda_2 x_2} dx_2 \left[ -\frac{1}{\lambda_1} e^{-\lambda_1 x_1} \right]^{a x_2}_0 = - \lambda_2 \int_0^\infty e^{- \lambda_2 x_2} dx_2 ( e^{-\lambda_1 a x_2} - 1 )[/tex]

[tex] = \lambda_2 \int_0^\infty e^{- \lambda_2 x_2} dx_2 -\lambda_2 \int_0^\infty e^{-\lambda_2 x_2 - \lambda_1 a x_2} dx_2 = \lambda_2 \int_0^\infty e^{- \lambda_2 x_2} dx_2 - \lambda_2 \int_0^\infty e^{-x_2(\lambda_2 + \lambda_1 a)} dx_2[/tex]

[tex]= \lambda_2 ( - \frac{1}{\lambda_2} ) \left[ e^{- \lambda_2 x_2 } \right]_0^\infty - \lambda_2 \frac{-1}{\lambda_2 + \lambda_1 a} \left[ e^{-x_2(\lambda_2 + \lambda_1 a) \right]_0^{\infty} = 1 - \frac{\lambda_2}{\lambda_2 + \lambda_1 a} [/tex]

So [tex]f_{X_1/X_2}(a) = f_Z(a) = \frac{d F_Z(a)}{da} = \frac{\lambda_1 \lambda_2}{(\lambda_1 a + \lambda_2)^2}[/tex]

Does this look like I'm doing it right?

Thanks.
 
Last edited:
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  • #2
I'm not an expert in this, but your reasoning all makes sense to me and your math does check out.
 

What is the distribution of two independent exponential random variables?

The distribution of two independent exponential random variables is known as the bivariate exponential distribution. It is a continuous probability distribution that describes the joint behavior of two independent exponential random variables.

How is the bivariate exponential distribution defined?

The bivariate exponential distribution is defined by two parameters, λ and μ, which represent the rate or scale of the exponential distributions for each variable. The probability density function for this distribution is given by f(x,y) = λμe-λx-μy for x ≥ 0 and y ≥ 0.

What is the relationship between the bivariate exponential distribution and the chi-square distribution?

The bivariate exponential distribution is closely related to the chi-square distribution. If two independent exponential random variables, X and Y, are squared and summed, the resulting distribution will follow a chi-square distribution with df = 2. This is known as the chi-square mixture of exponentials.

How is the bivariate exponential distribution used in practice?

The bivariate exponential distribution is commonly used in reliability and survival analysis. It can be used to model the failure times of two independent components in a system, and can be extended to model the behavior of more than two components. It is also used in queuing theory to model the time between arrivals and the service times of customers in a queue.

How can the bivariate exponential distribution be simulated?

The bivariate exponential distribution can be simulated using the inverse transform method. This involves generating two independent exponential random variables using a uniform random number generator, and then combining them using the inverse of the cumulative distribution function. There are also specialized software packages that can be used to simulate this distribution, such as the R package mvtnorm.

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