Conditional expectation proof question

In summary, the definition of E(X|Y) states that it is the function of Y that satisfies E(Xg(X)) = E(E(X|Y)g(Y)) for any function g. Using this definition, we can show that E(X1 + X2|Y) = E(X1|Y) + E(X2|Y). This is done by plugging in X = X1 + X2 and simplifying the equation to E(g(Y) [E(X1|Y) + E(X2|Y)]. It is important to note that the variables Y should be capitalized as Y.
  • #1
oyth94
33
0
Here is a proof question: For two random variables X and Y, we can define E(X|Y) to be the function of Y that satisfies E(Xg(X)) = E(E(X|Y)g(Y)) for any function g. Using this definition show that E(X1 + X2|Y) = E(X1|Y) + E(X2|Y)

So what I did was I plugged into X = X1 + X2
E(E(X1 + X2)|Y)g(Y))
= E(X1g(Y)) + E(X2g(Y))
= E(E(X1|y)g(Y) + E(X2|Y)g(Y))
= E(g(Y) [E(X1|Y) + E(X2|Y)]

am I on the right track? what do I do after that?
 
Physics news on Phys.org
  • #2
Are the $y$ supposed to be $Y$?
 

Related to Conditional expectation proof question

1. What is conditional expectation?

Conditional expectation is a statistical concept that represents the expected value of a random variable given the knowledge of another random variable. It is calculated by taking the average of all possible outcomes of the first random variable, while holding the second random variable constant.

2. How is conditional expectation different from regular expectation?

Regular expectation gives the average value of a random variable without considering any other variables. On the other hand, conditional expectation takes into account the knowledge of another variable and gives a more accurate representation of the expected value.

3. What is the formula for calculating conditional expectation?

The formula for conditional expectation is E[X|Y] = ∑x P(X=x|Y=y), where X and Y are random variables and x and y are their respective outcomes. This formula represents the sum of all possible outcomes of X, weighted by the probability of each outcome occurring given the value of Y.

4. What is the importance of conditional expectation in statistics?

Conditional expectation is important in statistics as it allows us to make more accurate predictions and inferences by taking into account the relationship between two random variables. It is also commonly used in regression analysis and can help identify patterns and relationships in data.

5. How is conditional expectation used in real-world applications?

Conditional expectation has various applications in fields such as finance, economics, and machine learning. It is used to predict stock prices, analyze consumer behavior, and improve decision-making processes. For example, in insurance, conditional expectation is used to calculate the expected loss given a specific risk profile.

Similar threads

  • Set Theory, Logic, Probability, Statistics
2
Replies
54
Views
4K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
553
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
986
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • MATLAB, Maple, Mathematica, LaTeX
Replies
5
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
862
  • Set Theory, Logic, Probability, Statistics
Replies
14
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
3K
Back
Top