Cauchy Distribution Homework: X/Y Derivation

In summary, the author is trying to solve a probability problem, but is stuck. He has identified that Z1=V*W and Z2=V, but does not know how to calculate the probability density function of W. He asks for help from the audience.
  • #1
dirk_mec1
761
13

Homework Statement



http://img132.imageshack.us/img132/1/48572399ly5.png

The Attempt at a Solution


I tried dividing the two pdf's but that isn't right. If you have X normal and Y normal distributed how can you derive the distribution function of X/Y?
 
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  • #2
Of course you don't divide the pdf's. To get the quotient pdf as a function of Z, you take the dirac delta function [itex]\delta(Z-X/Y)[/itex] and integrate it times the pdf's for X and Y, dXdY.
 
  • #3
Another method (based techniques typically presented early in mathematical stats) is this:

Define these two variables (one you already have)

[tex]
W = \frac{Z_1}{Z_2}, \quad V = Z_2
[/tex]

Then

[tex]
Z_1 = V \cdot W, \quad Z_2 = V
[/tex]

By their definitions both new random variables range over [tex] (-\infty, \infty) [/tex].

Use the basic ideas for transformation of a joint distribution to get the distribution of [tex] V [/tex] and [tex] W [/tex], then integrate out [tex] V [/tex].
 
  • #4
Hi,

I'm actually going over some probability problems and I got a bit stuck in this one too.

If you let:

W=Z1/Z2 and V=Z2

Then truly Z1=V*W and Z2=V

And if you calculate the Jacobian determinant of such transformations you get:

Jacobian determinant = V (here we take the absolute value when sticking into formula below)

Therefore:

f(w,v) =[PLAIN]http://www3.wolframalpha.com/Calculate/MSP/MSP91219bfg00e7b70caif00005aa636h4egb540i1?MSPStoreType=image/gif&s=39&w=148&h=44

and so all you need to get the probability density function of W is to integrate the joint probability with respect to v as follows:

First note that: d/dv (e-v2(1+w2)/2) = -v(1+w2)*e-v2(1+w2)/2

=>[PLAIN]http://www3.wolframalpha.com/Calculate/MSP/MSP243119bff8ch835e1b7i00001639e2c5d96b97gg?MSPStoreType=image/gif&s=39&w=366&h=54

and here is where I seem to be overlooking something, in order to get f(w) you must evaluate the integral from minus infinity to plus infinity and so I believe you get:

[PLAIN]http://www3.wolframalpha.com/Calculate/MSP/MSP237019bff8ch83i380ib0000641a5786ggg043f3?MSPStoreType=image/gif&s=39&w=124&h=43

Which is just plainly equal to zero, so I must've done something wrong, can anyone spot what was it? I would appreciate if someone did. Thanks.
 
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Related to Cauchy Distribution Homework: X/Y Derivation

1. What is a Cauchy distribution?

A Cauchy distribution is a continuous probability distribution that is used to model random variables with a symmetric distribution around a central value. It is also known as the Lorentz distribution and is similar to the normal distribution, but with much heavier tails.

2. How is the Cauchy distribution different from the normal distribution?

The Cauchy distribution differs from the normal distribution in that it has much heavier tails, meaning that it has a higher probability of extreme values. This is due to the fact that the Cauchy distribution has infinite variance, while the normal distribution has finite variance. Additionally, the Cauchy distribution is not a bell-shaped curve like the normal distribution, but instead has a peak at the center and long, thin tails.

3. What is the formula for the Cauchy distribution?

The probability density function (PDF) for the Cauchy distribution is given by f(x) = 1/(π(1 + (x-μ)^2/σ^2)), where μ is the location parameter and σ is the scale parameter. The cumulative distribution function (CDF) for the Cauchy distribution is given by F(x) = (1/π)tan^-1((x-μ)/σ) + 1/2.

4. How is the X/Y derivation used in Cauchy distribution homework?

The X/Y derivation is used to find the distribution of the ratio of two independent random variables that are both Cauchy distributed. This is helpful in solving problems related to Cauchy distribution homework, such as finding the probability of a certain range of values for the ratio of two variables.

5. Are there any real-world applications of the Cauchy distribution?

Yes, the Cauchy distribution has many real-world applications, especially in physics and engineering. It is often used to model random noise or measurement errors in experiments or studies. It is also used in financial modeling, specifically in the study of stock prices and market fluctuations. Additionally, the Cauchy distribution is used in image processing and computer vision tasks, such as edge detection and object recognition.

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