A simple conditional expectation question

In summary: It's not a counter example since the dice are still random. If it were a counter example, then the dice would have to be completely random and have no correlation between them. In summary, two dice can be used as a random example to show that the converse is not always true.
  • #1
michonamona
122
0
Let v be a random variable distributed according to F(.). Let X be a set containing the objects x1 and x2. Suppose

E(v|x1) = b AND E(v|x2) = b (The expected value of v conditional on x1 is b, etc)

where b is some constant.

Does it follow that E(v|x1,x2) = b? If so, why?



Additionally, does the converse hold?

i.e. does E(v|x1,x2) = b imply E(v|x1) = b and E(v|x2) = b?


Thank you!
M
 
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  • #2
Your converse is not true. Since the "If" part is always true for some b, it does not follow that every subdivision gives equal values. Find a random two cases E(v|x1) not equal to E(v|x2) any example of this would give you a counter example when you set b equal to E(v|x1,x2).

The original holds (I'm pretty sure) and you can verify it by simply breaking down the (conditional) expectation values into their definitions, sums over values times the corresponding (conditional) probabilities. You then use the P(A|B) = P(A and B)/P(B) formula and some algebra...at least that's how I'd start. There might be a lot of regrouping terms in a sum of sums but you should be able to manipulate definition of one side to a definition of the other.
 
  • #3
michonamona said:
Let v be a random variable distributed according to F(.). Let X be a set containing the objects x1 and x2. Suppose

E(v|x1) = b AND E(v|x2) = b (The expected value of v conditional on x1 is b, etc)

where b is some constant.

Does it follow that E(v|x1,x2) = b?
Roll two dice. v is total, x1 is first die showing 1, x2 is second showing 1.
 
  • #4
Is this a counter example?
 
  • #5
michonamona said:
Is this a counter example?
That's what I'm suggesting.
 

What is a simple conditional expectation?

A simple conditional expectation is a statistical concept that calculates the expected value of a random variable given some information or conditions. It represents the average outcome of a variable based on a specific scenario or set of conditions.

How is simple conditional expectation calculated?

Simple conditional expectation can be calculated by multiplying the probability of each possible outcome by its corresponding value, and then adding all of these products together. This calculation is often represented as E[X|Y] where X is the random variable and Y is the condition or information we have about it.

What is the difference between simple conditional expectation and regular expectation?

The main difference between simple conditional expectation and regular expectation is that simple conditional expectation takes into account a specific scenario or condition, while regular expectation considers all possible outcomes with equal weight. In other words, simple conditional expectation is a more focused or specific measure of expectation.

What is the purpose of using simple conditional expectation in statistical analysis?

Simple conditional expectation is a useful tool in statistical analysis as it allows us to make more accurate predictions and decisions by taking into account specific conditions or information. It also helps us understand the relationship between variables and how they may be affected by certain factors.

Can simple conditional expectation be used in any type of data analysis?

Yes, simple conditional expectation can be applied in a variety of data analysis situations, including regression analysis, Bayesian analysis, and time series analysis. It is a fundamental concept in statistics and is commonly used in various fields such as economics, finance, and social sciences.

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