Expectation of expansion of sum of independent random variables raised to some power.

Win_odd Dhamnekar

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Suppose [imath]X_1, X_2, \dots[/imath] are independent random variables with [imath]\mathbb{P}[X_i=1]=\frac13[/imath] and [imath]\mathbb{P}[X_i= -1] =\frac23[/imath].
Let [math]S_n = X_1 +\dots +X_n[/math] and let [imath]\mathcal{F}_n[/imath] denote the information in [imath] X_1, \dots, X_n[/imath]

1.Find[math]\mathbb{E}[S_n], \mathbb{E}[S^2_n], \mathbb{E}[S^3_n][/math]2. If m < n Find [math]E[S_n| \mathcal{F}_m], E[S^2_n|\mathcal{F}_m], E[S^3_n|\mathcal{F}_m][/math]3. If m < n , Find [math]E[X_m|S_n][/math]
How to answer all these questions?

I am working on these questions. Any math help will be accepted.:)
 
1.
[math]E(S_n) = E(X_1+\dots+X_n)=E(nX_i)=nE(X_i)\\ E(S_n^2)=\dots = n^2E(X_i^2)\\ E(S_n^3)=\dots = n^3E(X_i^3)[/math]
 
Agree with the first.

Didn't have time to check the other 2, but it doesn't feel right. Try with small cases like n=2, 3 to see if your formulas work.
Correct answers are [math]\mathbb{E}[S^2_n]= n + \frac{n(n-1)}{2}, \mathbb{E}[S^3_n]= -\frac{n}{3}-n(n-1)- \frac{n(n-1)}{9}[/math]
 
I assumed the RVs are symmetric above.

\(\displaystyle E(X_i) = -\dfrac{1}{3}\)

\(\displaystyle E(X_i^2) = 1\)

\(\displaystyle E(X_i^3) = -\dfrac{1}{3}\)




\(\displaystyle E(S_n) = nE(X_i) = \boxed{-\dfrac{n}{3}}\)

\(\displaystyle E(S_n^2) = nE(X_i^2) + n(n-1)E(X_i)^2 = \boxed{n + \dfrac{n(n-1)}{9}}\)

\(\displaystyle E(S_n^3) = nE(X_i^3) + 3n(n-1)E(X_i^2)E(X_i) +n(n-1)(n-2)E(X_i)^3 = \boxed{-\dfrac{n}{3} - n(n-1) -\dfrac{ n(n-1)(n-2)}{27}} \)
 
I assumed the RVs are symmetric above.

\(\displaystyle E(X_i) = -\dfrac{1}{3}\)

\(\displaystyle E(X_i^2) = 1\)

\(\displaystyle E(X_i^3) = -\dfrac{1}{3}\)




\(\displaystyle E(S_n) = nE(X_i) = \boxed{-\dfrac{n}{3}}\)

\(\displaystyle E(S_n^2) = nE(X_i^2) + n(n-1)E(X_i)^2 = \boxed{n + \dfrac{n(n-1)}{9}}\)

\(\displaystyle E(S_n^3) = nE(X_i^3) + 3n(n-1)E(X_i^2)E(X_i) +n(n-1)(n-2)E(X_i)^3 = \boxed{-\dfrac{n}{3} - n(n-1) -\dfrac{ n(n-1)(n-2)}{27}} \)
I like this version of [imath]E(S_n^2)[/imath] better :)
 
I assumed the RVs are symmetric above.

\(\displaystyle E(X_i) = -\dfrac{1}{3}\)

\(\displaystyle E(X_i^2) = 1\)

\(\displaystyle E(X_i^3) = -\dfrac{1}{3}\)




\(\displaystyle E(S_n) = nE(X_i) = \boxed{-\dfrac{n}{3}}\)

\(\displaystyle E(S_n^2) = nE(X_i^2) + n(n-1)E(X_i)^2 = \boxed{n + \dfrac{n(n-1)}{9}}\)

\(\displaystyle E(S_n^3) = nE(X_i^3) + 3n(n-1)E(X_i^2)E(X_i) +n(n-1)(n-2)E(X_i)^3 = \boxed{-\dfrac{n}{3} - n(n-1) -\dfrac{ n(n-1)(n-2)}{27}} \)
[imath]E[S_n | \mathcal{F}_n] = E[S_m | \mathcal{F}_n] +E[S_n - S_m|\mathcal{F}_n] = S_m + \mathbb{E}[S_n-S_m] = S_m + \mathbb{E}[X_j] (n -m) = S_m - \frac13 (n-m)[/imath]

We know [imath]E[X_j]=-\frac13 , \mathbb{E}[X^2_j]= 1[/imath] for each j.
Then if m < n ,
[math]E[S^2_n| \mathcal{F}_m] = E([S_m + (S_n - S_m)]^2 | \mathcal{F}_m) [/math][math]E[S^2_n |\mathcal{F}_m] = E[ S^2_m |\mathcal{F}_m] + 2 E [S_m (S_n -S_m)| \mathcal{F}_m] + E [(S_n -S_m)^2 | \mathcal{F}_m ][/math]
Since [imath]S_m[/imath] is [imath]\mathcal{F}_n[/imath] -measurable and [imath]S_n - S_m[/imath] is independent of [imath]\mathcal{F}_m[/imath]

[imath]E[S^2_m|\mathcal{F}_m] = S^2_m[/imath]
[imath]E[S_m(S_n -S_m) | \mathcal{F}_m]= S_m E[ S_n - S_m |\mathcal{F}_m]= S_m E[S_n - S_m] = -\frac{S_m}{3}[/imath]

[imath]E[(S_n -S_m)^2 |\mathcal{F}_m] = \mathbb{E}[(S_n -S_m)^2] = Var (S_n - S_m) = (E[X^2_j] - E[X_j])(n-m) = \frac43 (n-m)[/imath]

and hence,
[math]E[S^2_n |\mathcal{F}_m] = S^2_m -\frac{S_m}{3} +\frac43(n-m)[/math]
Are these above answers correct?

Note : Above answers are computed by referring to G.F. Lawler's Book "Stochastic Calculus: An Introduction with Applications"

How to compute [imath]E[ S^3_n|\mathcal{F}_m]?[/imath]
 
[imath]E[S_n | \mathcal{F}_n] = E[S_m | \mathcal{F}_n] +E[S_n - S_m|\mathcal{F}_n] = S_m + \mathbb{E}[S_n-S_m] = S_m + \mathbb{E}[X_j] (n -m) = S_m - \frac13 (n-m)[/imath]

We know [imath]E[X_j]=-\frac13 , \mathbb{E}[X^2_j]= 1[/imath] for each j.
Then if m < n ,
[math]E[S^2_n| \mathcal{F}_m] = E([S_m + (S_n - S_m)]^2 | \mathcal{F}_m) [/math][math]E[S^2_n |\mathcal{F}_m] = E[ S^2_m |\mathcal{F}_m] + 2 E [S_m (S_n -S_m)| \mathcal{F}_m] + E [(S_n -S_m)^2 | \mathcal{F}_m ][/math]
Since [imath]S_m[/imath] is [imath]\mathcal{F}_n[/imath] -measurable and [imath]S_n - S_m[/imath] is independent of [imath]\mathcal{F}_m[/imath]

[imath]E[S^2_m|\mathcal{F}_m] = S^2_m[/imath]
[imath]E[S_m(S_n -S_m) | \mathcal{F}_m]= S_m E[ S_n - S_m |\mathcal{F}_m]= S_m E[S_n - S_m] = -\frac{S_m}{3}[/imath]

[imath]E[(S_n -S_m)^2 |\mathcal{F}_m] = \mathbb{E}[(S_n -S_m)^2] = Var (S_n - S_m) = (E[X^2_j] - E[X_j])(n-m) = \frac43 (n-m)[/imath]

and hence,
[math]E[S^2_n |\mathcal{F}_m] = S^2_m -\frac{S_m}{3} +\frac43(n-m)[/math]
Are these above answers correct?

Note : Above answers are computed by referring to G.F. Lawler's Book "Stochastic Calculus: An Introduction with Applications"

How to compute [imath]E[ S^3_n|\mathcal{F}_m]?[/imath]
Are you done with [imath]E(S_n^2)[/imath] ?
 
Are you done with [imath]E(S_n^2)[/imath] ?
[imath]E[S^2_n]= n +\displaystyle\frac{n(n-1)}{9}[/imath]

In #8 , all the answers are correctly stated for question 1 considering multinomial theorem for [imath]( x_1 + \dots + x_k)^n [/imath]
 
Errata [imath] [S^2_n|\mathcal{F}_m] = S^2_m -\frac23S_m + (n- m)[/imath]



[imath]E[S^3_n|\mathcal{F}_m]= E([ S_m + (S_n - S_m)]^3|\mathcal{F}_m)[/imath]

[imath]E[S^3_n|\mathcal{F}_m] = E[S^3_n|\mathcal{F}_m] + 3 E[S^2_m(S_n-S_m)|\mathcal{F}_m] + 3E[ S_m(S_n - S_m)^2 | \mathcal{F}_m] + E[(S_n -S_m)^3| \mathcal{F}_m][/imath]

Since[imath]S_m[/imath] is [imath]\mathcal{F}_m [/imath] -measurable and [imath]S_n - S_m[/imath] is independent of [imath]\mathcal{F}_m[/imath]

[math]E[S^3_m|\mathcal{F}_m] = S^3_m[/math]
[imath] E[S^2_m(S_n -S_m )|\mathcal{F}_m] = S^2_m E[(S_n -S_m)|\mathcal{F}_m] = S^2_m \mathbb{E}[ S_n -S_m] = -\displaystyle\frac{S^2_m}{3}[/imath]
=
[imath] E[S_m )(S_n -S_m)^2|\mathcal{F}_m] = S_m E[(S_n -S_m)^2 |\mathcal{F}_m]= S_m E[X^2_j] = S_m (1) = S_m[/imath]

[imath]E[(S_n -S_m)^3 |\mathcal{F}_m]= E[X^3_j]= -\displaystyle\frac13 (n-m)[/imath]

[imath]E[S^3_n|\mathcal{F}_m] = S^3_m - S^2_m + 3 S_m - \displaystyle\frac13 (n-m ) [/imath]

3.How to find [imath]E[X_m | S_n][/imath] given that m < n.
 
Suppose [imath]X_1, X_2, \dots[/imath] are i.i.d. random variables. We will compute [imath]E[X_1| S_n][/imath]
Note that the information contained in the one data point [imath]S_n[/imath] is less than the information contained in [imath]X_1, \dots , X_n.[/imath] However, since the random variables are independent and identically distributed, it must be the case that[math]E[X_1|S_n ] = E[X_m| S_n ] = E[X_n |S_n][/math]
Linearity implies that
[math]m E[X_1|S_n] =\displaystyle\sum_{j=1}^m E[X_j|S_n] = E[ X_1 +\dots + X_n| S_n] = E[S_m |S_n] = S_m[/math]
[math]\therefore E[X_m|S_n] =\frac{S_m}{m}[/math]
 
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