Questions about Linear Combinations of Random Variables

In summary, the conversation discusses finding the moment-generating function and p.d.f of a random variable Y, which involves a weighted sum of squares of independent, mean-0 random variables. The conversation also brings up the difficulty of finding the p.d.f of Y when the random variables are raised to higher powers. The conversation also mentions a hint involving the sum of squares of independent random variables and asks for clarification on the properties involved.
  • #1
ken15ken15
4
0

Homework Statement


RQwRhjc.jpg

Homework Equations


Y=1/2*(X1-X3)^2+1/14*(X2+2X4-3X5)^2

The Attempt at a Solution


For (a) part, I have only learned to find the moment-generating function of Y, but not finding the p.d.f.
Moreover, the examples I have seen only involves random variables Xi to the power 1, but not to higher power.

For (b) part, the difficulty for me is just similar to part (a).
 
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  • #2
Hint: [tex] (Z_1^2 + ... +Z_n^2) +(Z_1^2 + ... +Z_m^2) = (Z_1^2 + ... +Z_n^2 +...+ Z_m^2) [/tex]
 
  • #3
dirk_mec1 said:
Hint: [tex] (Z_1^2 + ... +Z_n^2) +(Z_1^2 + ... +Z_m^2) = (Z_1^2 + ... +Z_n^2 +...+ Z_m^2) [/tex]

sorry but...what property is it? I haven't learned this before...
 
  • #4
ken15ken15 said:

Homework Statement


RQwRhjc.jpg



Homework Equations


Y=1/2*(X1-X3)^2+1/14*(X2+2X4-3X5)^2


The Attempt at a Solution


For (a) part, I have only learned to find the moment-generating function of Y, but not finding the p.d.f.
Moreover, the examples I have seen only involves random variables Xi to the power 1, but not to higher power.

For (b) part, the difficulty for me is just similar to part (a).

##Z_1 = X_1 - X_3## has an ##N(0,a)## distribution, ##Z_2 = X_2+2X_4-3X_5## is ##N(0,b)##, and ##Z_1, Z_2## are independent. So ##Y## involves a weighted sum of squares of independent, mean-0 random variables.
 
  • #5
Ray Vickson said:
##Z_1 = X_1 - X_3## has an ##N(0,a)## distribution, ##Z_2 = X_2+2X_4-3X_5## is ##N(0,b)##, and ##Z_1, Z_2## are independent. So ##Y## involves a weighted sum of squares of independent, mean-0 random variables.

So do you mean given the random variables are independent, the sum or difference of them will also have the same distribution of them? Also, how can a duel with the square of the random variables? Do I need to consider it with transformation of variables?
 

Related to Questions about Linear Combinations of Random Variables

1. What is a linear combination of random variables?

A linear combination of random variables is a mathematical expression that is formed by multiplying each random variable by a constant and then summing them together. It can also be thought of as a weighted average of the random variables.

2. Why are linear combinations of random variables important?

Linear combinations of random variables are important because they allow us to model and analyze complex systems by breaking them down into simpler components. They also help us understand the relationship between different variables and how they contribute to an outcome.

3. How do you calculate the mean of a linear combination of random variables?

The mean of a linear combination of random variables can be calculated by multiplying each random variable by its corresponding constant, and then summing them together. This can be represented by the formula E(aX + bY) = aE(X) + bE(Y), where E denotes the expected value.

4. Can the variance of a linear combination of random variables be negative?

No, the variance of a linear combination of random variables cannot be negative. The variance is a measure of the spread of the values around the mean, and it cannot be negative as it is squared in the calculation. If a linear combination of random variables has a negative variance, it would imply that some of the values are negative, which is not possible for random variables.

5. How are linear combinations of random variables used in real-world applications?

Linear combinations of random variables are used in various fields such as finance, economics, and engineering to model and analyze complex systems. In finance, they are used to calculate the expected returns and risk of investment portfolios. In economics, they help understand the relationship between different factors and their impact on outcomes. In engineering, they are used in signal processing and control systems to model and predict behavior.

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