1. ## Expected Value

Hi,

Suppose E(U|X) = X^2. Suppose that E(X) = 0, Var(X) = 1 and E(X^3) = 0

What is the E(UX)?

Thanks alot for helping!

2. Hello,

$E[UX]=E[E[UX|X]]=E[XE[U|X]]=E[X^3]$

3. @ Moo - Thanks for your response.

But I am not confortable with all the theorms/or rather properties of Expectation (in case of joint distributions). I basically get confused and at times rely on intuition.

Will it be possible for you to reccomend me
1. A source/book I can read to understand this better
2. For e.g. Why is
E(Y) = E(E(Y|X))
And then how you got
E[E[UX|X]]=E[XE[U|X]] - I understand it intuitively but not rigorously.

Thanks

4. E(Y) = E(E(Y|X))
is easy to prove, just write the conditional expectation and integrate/sum a second time

5. Thanks.

But where I'm weak is a better understanding of Joint Distributions. I tried referring some online resources but couldn't get a very clear idea. And, I'm not attending any university so little hard for me to find out where can you read up on it. Thanks

7. In the continuous setting....

$E(Y|X)=\int yf_{Y|X}(y)dy$

$=\int y{f_{X,Y}(x,y)\over f_X(x)}dy$

now take the expectation wrt x...

$E(E(Y|X))=\int E(Y|X)f_X(x)dx$

and wave the Fubini wand...

$E(E(Y|X))=\int \int y{f_{X,Y}(x,y)\over f_X(x)}dyf_X(x)dx$

$=\int \int yf_{X,Y}(x,y)dydx=E(Y)$

8. aman_cc,

I have a very formal proof of the fact that $E[E[X|Y]]=E[X]$ but it's kind of long, and very theoretical. You also need measure theory knowledge. Well I don't think it's a relevant one...

The general idea is to say that in a probability space $\Omega,\mathcal A,\mathbb P)$ and given a sigma-algebra $\mathcal B$, and X a random variable $\in L^1(\Omega,\mathcal A,\mathbb P)$, then there exists a unique random variable $Z=E[X|\mathcal B$ such that for any $B\in \mathcal B,\int_B Z d\mathbb P=\int_B Xd\mathbb P$.
In particular, for $B=\Omega$, we get the desired equality.

Note that when we write $E[X|Y]$, it's in fact $E[X|\sigma(Y)$, where $\sigma(Y)$ is the sigma-algebra generated by $Y$. So it's in place of $\mathcal B$ in the above paragraph.

Also note that with the Aussie singer's method, matheagle, we have to assume that these random variables have a pdf.

For the second point, the proof doesn't look that difficult, it's just long and painful... I don't know how to explain it with words...

If there is a good book at an intermediate level to read up on probabolity thoery and expectations, distribution (joint) etc please do refer that to me. I like books which start with clear definitions of concepts / axioms and then proceed to prove such theorms for there. You can consider me a novice at measure theory.

10. Hi,

I know three books in English that look good. But I don't know if they're the best. My library isn't specialized in English books

Amazon.com: Probability with Martingales (Cambridge Mathematical Textbooks) (9780521406055): David Williams: Books
Amazon.com: Probability and Measure, 3rd Edition (9780471007104): Patrick Billingsley: Books
Measure, Integral and Probability: Amazon.fr: Marek Capinski, Peter E. Kopp, Ekkehard Kopp: Livres en anglais

well they're expensive, but I hope the library in your university has some of them !