Conditional expectation in statistics

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Homework Help Overview

The discussion revolves around the relationship between conditional expectation and covariance in statistics, specifically examining the condition when E[W|X]=0 and its implications for Cov(W,X).

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants explore the validity of the statement that E[W|X]=0 implies Cov(W,X)=0, with some questioning the generality of this claim. They discuss definitions of covariance and conditional expectation, and how these relate to the problem at hand.

Discussion Status

There is an ongoing exploration of the assumptions and definitions involved in the problem. Some participants have provided insights into the conditions under which the original statement might hold true, while others are questioning the steps taken in the reasoning process. No consensus has been reached yet.

Contextual Notes

Participants note that the original poster's assumption of E[W] being zero is not explicitly stated in the problem, which raises questions about the validity of certain transitions in the reasoning. There is also mention of the independence of W and X as a factor that could simplify the relationship.

libragirl79
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Hi,

I am trying to show that if the E[W|X]=0 then the Cov (W,X)=0.




Using the def of variance, and given that E[W] is zero,
I get that Cov is equal to: E[WX]-E[W * E(X)]

using conditional expectation:

E [E(WX|X)] -E[x]E[W]= E[X E[W|X]]-E[X]E[E(W|X)]=0

I am not sure if this transition (in red) is ok.

Thanks!
 
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libragirl79 said:
Hi,

I am trying to show that if the E[W|X]=0 then the Cov (W,X)=0.
The difficulty is that this statement is, in general, NOT true.
What is true is that if E[W|X]= E[W] then Cov(W, X)= 0.



Using the def of variance, and given that E[W] is zero,
What? Where is that gven?

I get that Cov is equal to: E[WX]-E[W * E(X)]

using conditional expectation:

E [E(WX|X)] -E[x]E[W]= E[X E[W|X]]-E[X]E[E(W|X)]=0

I am not sure if this transition (in red) is ok.

Thanks!
 
Thanks for the reply!

From what I understand the posed statement is true when E[W|X]=0 a.s.

E[W]=E[E[W|X]]=E[0]=0 and the def of cov being E[(W-E(W))(X-E(X))]

The question I have is if Expec value of two vars, WX, can be conditioned as I wrote above on X and then X extracted...
 
libragirl79 said:
Thanks for the reply!

From what I understand the posed statement is true when E[W|X]=0 a.s.

E[W]=E[E[W|X]]=E[0]=0 and the def of cov being E[(W-E(W))(X-E(X))]

The question I have is if Expec value of two vars, WX, can be conditioned as I wrote above on X and then X extracted...

Cov(W,X) = E(W*X) - EW * EX (a standard result). Can you relate E(W*X) and EW to E(W|X)?
 
The only way I know how to relate the two was by doing the conditioning I did of both WX on X...It would be optimal if W and X were indep, then the stand result for Cov works perfectly, but that's not the case here...
 

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