Bivariate Transformation of Random Variables

In summary, the conversation discussed finding the pdf of Y = X1X2 where X1 and X2 are continuous random variables with a joint pdf. The attempted solution involved using a transformation shortcut and considering different choices for Z, but the integral did not converge. It was also noted that the product of X1 and X2 will be smaller than X1 alone since they are both smaller than 1. However, it is unclear if the chosen transformation is valid in this situation.
  • #1
Yagoda
46
0

Homework Statement


Two RVs X1 and X2 are continuous and have joint pdf [itex]
f_{X_1,X_2}(x_1, x_2) = \begin{cases} x_1+x_2 &\mbox{for } 0 < x_1 < 1; 0 < x_2 < 1
\\
0 & \mbox{ } \text{otherwise}. \end{cases} [/itex]

Find the pdf of [itex]Y = X_1X_2[/itex].

Homework Equations


I'm using the transformation "shortcut' that says if U = g1(x,y), V = g2(x,y) and h1(u,v) = x, h2(u,v) = y then
[itex]f_{U,V}(u,v) = f_{X,Y}(h_1(u,v), h_2(u,v) \times |J| [/itex] where |J| is the absolute value of the determinant of the Jacobian of h1 and h2.

The Attempt at a Solution


In this setting I let [itex]Y = X_1X_2[/itex] and [itex]Z = X_1[/itex] so [itex]h_1(y,z) = Z[/itex] and [itex]h_2(y,z) = Y/Z[/itex]. Then I calculated [itex]|J| = \frac{1}{Z}[/itex] so plugging in I got that
[itex]f_{Y,Z}(y,z) = (z + \frac{y}{z})(\frac{1}{z}) = 1 + \frac{y}{z^2}[/itex] where [itex]0 < z < 1, 0 < y < z[/itex].
So to find the pdf of Y, integrate with respect to z over all possible values of z, but this integral does not converge when evaluated from 0 to 1.

I've tried other choices for Z, but I've ended up with the same problem.
 
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  • #2
Note: if both [itex] 0 \le x_1 \le 1 [/itex] and [itex] 0 \le x_2 \le 1 [/itex], how does the product
[itex] X_1 X_2 [/itex] compare in size to [itex] X_1 [/itex] alone?

Edited to add: I have not stepped through all of your work to simplify the transformation, simply noted that you have [itex] 0 < z < 1 [/itex] and [itex] 0 < y < 1 [/itex].
 
  • #3
Since [itex]X_1[/itex] and [itex]X_2[/itex] are both smaller than 1, then their product will be smaller than [itex]X_1[/itex] alone, but I'm not seeing how this helps me find the pdf of [itex]Y[/itex].

Are the limits [itex]0 < y < z[/itex] not correct? It seems that this needs to be true in order for [itex]Y/Z[/itex] to be less than 1?

Now that I'm looking at it again, is this transformation shortcut even valid in this situation? I'm not sure if my [itex]h_1, h_2[/itex] are workable in this case.
 

What is bivariate transformation of random variables?

Bivariate transformation of random variables is a statistical method used to transform two random variables into a new set of variables. This is often done to simplify the analysis of complex data or to uncover relationships between the two variables.

Why is bivariate transformation of random variables important?

Bivariate transformation of random variables is important because it allows for the analysis of two variables simultaneously, which can reveal patterns and relationships that may not be apparent when analyzing each variable separately. This can provide a more comprehensive understanding of the data and can help in making more accurate predictions and decisions.

What are some common techniques used in bivariate transformation of random variables?

Some common techniques used in bivariate transformation of random variables include logarithmic transformation, power transformation, and Box-Cox transformation. These techniques aim to transform the data to meet the assumptions of statistical models and to make the relationship between the variables more linear.

How do I choose the appropriate transformation for my data?

Choosing the appropriate transformation for your data depends on the distribution and relationship between the two variables. It is best to consult a statistician or use visual aids such as scatter plots, histograms, and Q-Q plots to determine the most suitable transformation for your data.

Are there any limitations to bivariate transformation of random variables?

Yes, there are some limitations to bivariate transformation of random variables. The transformation can only be applied to continuous variables, and the results may be affected by outliers in the data. Additionally, the transformation may not always result in a perfect linear relationship between the variables, and further analysis may be necessary to fully understand the relationship between the variables.

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