How Do You Find the PDF of a Ratio of Exponential Random Variables?

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SUMMARY

The discussion focuses on finding the probability density function (PDF) of the random variable Z defined as z = x/(x + y), where X and Y are independent exponential random variables with parameter 1. The cumulative distribution function (CDF) F_Z(z) is derived through integration of the joint PDF f_{xy}(x,y) = f_x(x)f_y(y). The final result shows that the PDF of Z is z, but the initial approach to derive it was incorrect due to a misinterpretation of the inequality when z > 1. Alternative methods involving the reciprocal of Z are suggested for further exploration.

PREREQUISITES
  • Understanding of exponential random variables and their properties
  • Knowledge of joint probability density functions
  • Familiarity with cumulative distribution functions and their derivation
  • Concept of transformations of random variables
NEXT STEPS
  • Study the derivation of the PDF for the ratio of two independent exponential random variables
  • Learn about the properties of joint distributions and their applications in probability theory
  • Explore the method for finding the PDF of the reciprocal of a random variable
  • Investigate the implications of transformations in probability distributions
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Students and professionals in statistics, data science, and probability theory who are working with random variables, particularly those interested in the behavior of ratios of exponential distributions.

dionysian
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Homework Statement


Let X and Y be two independent random variables each exponentially distributed with parameter 1. Define a new random variable:

[tex]z = \frac{x}{{x + y}}[/tex]

Find the PDF of Z


Homework Equations





The Attempt at a Solution


[tex]\begin{array}{l}<br /> {F_Z}(z) = P(Z < z) = P\left( {\frac{x}{{x + y}} < z} \right) = P\left( {x \le \frac{{zy}}{{1 - z}}} \right) \\ <br /> {F_Z}(z) = \int\limits_0^\infty {\int_0^{\frac{{zy}}{{1 - z}}} {{f_{xy}}(x,y)dxdy} } \\ <br /> {f_{xy}}(x,y) = {f_x}(x){f_y}(y) \\ <br /> {F_Z}(z) = \int\limits_0^\infty {\int_0^{\frac{{zy}}{{1 - z}}} {{f_x}(x){f_y}(y)dxdy = } } \int\limits_0^\infty {\int_0^{\frac{{zy}}{{1 - z}}} {{e^{ - x}}{e^{ - y}}dxdy} } = \int\limits_0^\infty {{e^{ - y}}\left[ {\int_0^{\frac{{zy}}{{1 - z}}} {{e^{ - x}}dx} } \right]} dy \\ <br /> {F_Z}(z) = \int\limits_0^\infty {{e^{ - y}}\left[ { - {e^{ - \frac{{zy}}{{1 - z}}}} + 1} \right]} dy = \int\limits_0^\infty { - {e^{ - y}}{e^{ - \frac{{zy}}{{1 - z}}}} + {e^{ - y}}} dy = \int\limits_0^\infty { - {e^{ - \frac{{y(1 - z) - zy}}{{1 - z}}}} + {e^{ - y}}} dy \\ <br /> {F_Z}(z) = \int\limits_0^\infty { - {e^{ - \frac{y}{{1 - z}}}} + {e^{ - y}}} dy = (1 - z){e^{ - \frac{y}{{1 - z}}}}|_0^\infty - {e^{ - y}}|_0^\infty = z \\ <br /> \end{array}[/tex]
Now i know that if i take the derivative of this i will get the "pdf" but its obviously wrong. Any thoughts?
 
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dionysian said:
[tex]P\left( {\frac{x}{{x + y}} < z} \right) = P\left( {x \le \frac{{zy}}{{1 - z}}} \right)[/tex]

This step is invalid if [itex]z > 1[/itex]. (The inequality gets reversed in that case.)

But you could instead write

[tex]P\left( \frac{x}{x+y} < z \right) = P\left(y > \frac{x(1-z)}{z}\right) = 1 - P\left(y \leq \frac{x(1-z)}{z}\right)[/tex]

I'm not sure if that will be any more helpful, but at least it's correct.

I wonder if it would be helpful to work with the reciprocal:

[tex]\frac{1}{z} = \frac{x + y}{x} = 1 + \frac{y}{x}[/tex]

It shouldn't be hard to work out the pdf of

[tex]\frac{y}{x}[/tex]

as it is the quotient of two independent random variables. Adding 1 just shifts the pdf to the right by 1. Then do you know how to find the pdf of the reciprocal of a random variable with known pdf?
 
Last edited:

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