(Probability/Statistics) Transformation of Bivariate Random Variable

In summary, the conversation discusses finding the joint pdf of two random variables, Y_1 and Y_2, given the joint pdf of X_1 and X_2. The conversation also mentions the use of inverse functions and marginal distributions, and concludes with the solution involving the Jacobian.
  • #1
rayge
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Homework Statement



Let [itex]X_1, X_2[/itex] have the joint pdf [itex]h(x_1, x_2) = 8x_1x_2, 0<x_1<x_2<1 [/itex], zero elsewhere. Find the joint pdf of [itex]Y_1=X_1/X_2[/itex] and [itex]Y_2=X_2[/itex].

Homework Equations



[tex]p_Y(y_1,y_2)=p_X[w_1(y_1,y_2),w_2(y_1,y_2)][/tex] where [itex]w_i[/itex] is the inverse of [itex]y_1=u_1(x_1,x_2)[/itex]

The Attempt at a Solution


We can get [itex]X_1=Y_1Y_2[/itex] and [itex]X_2=Y_2[/itex]. Naively plugging in [itex]y[/itex], we can get [itex]8y_1y_2^2[/itex]. However this isn't right according to the back of the book.

I thought it might have to do with finding the marginal distributions of [itex]x_1, x_2[/itex], but that doesn't seem to lead me anywhere either. Any thoughts welcome!
 
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  • #2
rayge said:

Homework Statement



Let [itex]X_1, X_2[/itex] have the joint pdf [itex]h(x_1, x_2) = 8x_1x_2, 0<x_1<x_2<1 [/itex], zero elsewhere. Find the joint pdf of [itex]Y_1=X_1/X_2[/itex] and [itex]Y_2=X_2[/itex].

Homework Equations



[tex]p_Y(y_1,y_2)=p_X[w_1(y_1,y_2),w_2(y_1,y_2)][/tex] where [itex]w_i[/itex] is the inverse of [itex]y_1=u_1(x_1,x_2)[/itex]

The Attempt at a Solution


We can get [itex]X_1=Y_1Y_2[/itex] and [itex]X_2=Y_2[/itex]. Naively plugging in [itex]y[/itex], we can get [itex]8y_1y_2^2[/itex]. However this isn't right according to the back of the book.

I thought it might have to do with finding the marginal distributions of [itex]x_1, x_2[/itex], but that doesn't seem to lead me anywhere either. Any thoughts welcome!

You forgot the Jacobian, necessary to transform dx1*dx2 into h(y1,y2)*dy1*dy2
 
  • #3
Thanks! That was it.
 

FAQ: (Probability/Statistics) Transformation of Bivariate Random Variable

1. What is a bivariate random variable?

A bivariate random variable is a type of random variable that takes on two values, typically denoted by X and Y. It represents the outcomes of two different random experiments or events. For example, X could represent the number of heads when flipping a coin twice, and Y could represent the sum of two dice rolls.

2. What is the purpose of transforming bivariate random variables?

The purpose of transforming bivariate random variables is to simplify and analyze the data in a more meaningful way. By transforming the variables, we can often uncover patterns and relationships that may not have been apparent in the original data. It also allows us to make predictions and draw conclusions about the underlying population.

3. What is the difference between a linear and nonlinear transformation of bivariate random variables?

A linear transformation involves multiplying each value of X and Y by a constant and adding a constant. It results in a linear relationship between the transformed variables. On the other hand, a nonlinear transformation involves applying a mathematical function, such as taking the square root or logarithm, to the values of X and Y. This can result in a nonlinear relationship between the transformed variables.

4. How does the covariance change when transforming bivariate random variables?

When transforming bivariate random variables, the covariance may change depending on the type of transformation. For linear transformations, the covariance remains the same. However, for nonlinear transformations, the covariance can increase or decrease, depending on the strength and direction of the relationship between the variables.

5. Can transforming bivariate random variables affect the correlation coefficient?

Yes, transforming bivariate random variables can affect the correlation coefficient. For linear transformations, the correlation coefficient remains the same. However, for nonlinear transformations, the correlation coefficient can increase or decrease, depending on the strength and direction of the relationship between the variables. This is because the correlation coefficient measures the strength and direction of a linear relationship between two variables.

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