Conditional expectations question

In summary, the question is whether there exists a random variable V that is jointly distributed with X and has the same conditional expectation as W. This problem can be translated to calculus, but it is unclear if there is a solution unless the densities follow a specific relationship.
  • #1
Obie
6
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I have a simple-seeming question on conditional expectation. It seems simple, but it has eluded my attempt to answer.

Suppose that two jointly distributed random variables (X,Y) exist with support on positive real line.

From these, it is possible to construct a new random variable, Z=E[X|Y]. Of course, by Bayes rule, E[Z]=E[E[X|Y]]=E[X]. Support of Z is a subset of positive real line.

Suppose now that you are given some third random variable W, and you know that E[W]=E[X]. W hassupport on positive part of real line. Does there exist a random variable V, jointly distributed with X for which W=E[X|V].

Any help is appreciated...
 
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  • #2
Obie said:
. Does there exist a random variable V, jointly distributed with X for which W=E[X|V].

That is an interesting question. My attempt to translate it to calculus is:

Give the random variable W with density p(x) and the random variable X with density q(x), find f(x,y) satisfying

[tex] q(x) = \int f(x,y) dy [/tex]
[tex] p(x) = \int x f(x,y) dy [/tex]

The last equation implies [tex] \frac {p(x)}{x} = \int f(x,y) dy [/tex]

So are we out of luck unless [itex] q(x) = \frac{p(x)}{x} [/itex] ?
 

What is a conditional expectation?

A conditional expectation is a statistical concept that represents the expected value of a random variable given the knowledge of the value of another random variable. It is used to measure the average outcome of a variable based on a certain condition or set of conditions.

How is conditional expectation calculated?

Conditional expectation is calculated using the formula E(X|Y) = ∫x f(x|y) dx, where X is the random variable and Y is the known condition. This formula takes into account the probability distribution of X given Y and calculates the expected value of X.

Why is conditional expectation important?

Conditional expectation is important because it allows us to make predictions and decisions based on known conditions. It can help us understand the relationship between two variables and how one variable affects the expected value of the other. It is also frequently used in statistical and machine learning models.

What is the difference between conditional expectation and unconditional expectation?

The unconditional expectation, also known as the simple expectation, is the expected value of a random variable without any known conditions. It takes into account the entire probability distribution of the variable. On the other hand, conditional expectation considers a specific condition and calculates the expected value of the variable based on that condition.

How is conditional expectation used in real-world applications?

Conditional expectation has various real-world applications, such as in finance, economics, and insurance. In finance, it is used to predict stock prices based on market conditions. In economics, it can be used to estimate the impact of a policy change on an economy. In insurance, it is used to determine the risk of certain events occurring based on known conditions. It is also used in machine learning and data analysis to make predictions and decisions based on known variables.

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