Linear Combination of Independent Random Variables
I am working on a problem and am a bit stuck
The problem: For independent random variables
X ~ N(-1,3)
Y ~ N(0,2)
Z ~ N(4,1)
Consider U:= 2X - 4Y - Z + 5
Find the Expectation, variance, and MGF for U.
What I have done so far:
Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)
Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)
Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)
Then MGF of U:
Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =
e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =
exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =
exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =
U ~ N(-1, 22.5)
Then for Expectation:
F'u(t) =
But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?
probability variance moment-generating-functions expected-value
add a comment |
I am working on a problem and am a bit stuck
The problem: For independent random variables
X ~ N(-1,3)
Y ~ N(0,2)
Z ~ N(4,1)
Consider U:= 2X - 4Y - Z + 5
Find the Expectation, variance, and MGF for U.
What I have done so far:
Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)
Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)
Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)
Then MGF of U:
Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =
e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =
exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =
exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =
U ~ N(-1, 22.5)
Then for Expectation:
F'u(t) =
But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?
probability variance moment-generating-functions expected-value
add a comment |
I am working on a problem and am a bit stuck
The problem: For independent random variables
X ~ N(-1,3)
Y ~ N(0,2)
Z ~ N(4,1)
Consider U:= 2X - 4Y - Z + 5
Find the Expectation, variance, and MGF for U.
What I have done so far:
Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)
Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)
Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)
Then MGF of U:
Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =
e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =
exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =
exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =
U ~ N(-1, 22.5)
Then for Expectation:
F'u(t) =
But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?
probability variance moment-generating-functions expected-value
I am working on a problem and am a bit stuck
The problem: For independent random variables
X ~ N(-1,3)
Y ~ N(0,2)
Z ~ N(4,1)
Consider U:= 2X - 4Y - Z + 5
Find the Expectation, variance, and MGF for U.
What I have done so far:
Fx(t) = Ee$^{tX}$ = exp(-t + $3t^2over2$)
Fy(t) = Ee$^{tY}$ = exp(0t + t$^2$)
Fz(t) = Ee$^{tZ}$ = exp(4t + $t^2over2$)
Then MGF of U:
Fu(t) = Ee$^{t(2X-4Y-Z+5)}$ = E[e$^{(2t)X}$e$^{(-4t)Y}$e$^{(-t)Z}$e$^{(5t)}$] =
e$^{(5t)}$Ee$^{(2t)X}$Ee$^{(-4t)Y}$Ee$^{(-t)Z}$ = e$^{(5t)}$Fx(2t)Fy(-4t)Fz(-1t) =
exp(5t) exp(-(2t) + $3(2t)^2over2$) exp(0(-4t) + (-4t)$^2$) exp(4(-1t) + $(-1t)^2over2$) =
exp(5t -2t +0t -4t + 6t$^2$ +16t$^2$ + $1over2$$t^2$) = exp(-1t + 22.5t$^2$) =
U ~ N(-1, 22.5)
Then for Expectation:
F'u(t) =
But here is where I am stuck. How do we find the Expectation of U (how do we take this derivative) and how do we find the Variance of U? Also, does the first part look correct?
probability variance moment-generating-functions expected-value
probability variance moment-generating-functions expected-value
asked Nov 20 at 18:09
Ethan
10012
10012
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1 Answer
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Expecation is linear.
$$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$
Also, since they are independent.
$$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$
Remark:
- Usually we use $F$ for CDF and $M$ for mgf.
$U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
Expecation is linear.
$$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$
Also, since they are independent.
$$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$
Remark:
- Usually we use $F$ for CDF and $M$ for mgf.
$U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
add a comment |
Expecation is linear.
$$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$
Also, since they are independent.
$$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$
Remark:
- Usually we use $F$ for CDF and $M$ for mgf.
$U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
add a comment |
Expecation is linear.
$$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$
Also, since they are independent.
$$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$
Remark:
- Usually we use $F$ for CDF and $M$ for mgf.
$U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.
Expecation is linear.
$$E[U]= 2E[X] - 4E[Y] - E[Z] + 5$$
Also, since they are independent.
$$Var[U]= 2^2Var[X] + 4^2Var[Y] + Var[Z] $$
Remark:
- Usually we use $F$ for CDF and $M$ for mgf.
$U$ is again a normal distribution and after you find out the mean and variance, figuring out the mgf should be easy.
edited Nov 20 at 18:17
answered Nov 20 at 18:12
Siong Thye Goh
99.1k1464117
99.1k1464117
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
add a comment |
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
To clarify though, to find these expectations we need to take the derivative of the MFGs correct? ie. M'x(t) = M'x(0) = EX and M''x(t) = M''x(0) = E(X$^2$)
– Ethan
Nov 20 at 18:25
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
that's just one of the way. but here we are given that $X sim N(-1,3)$, clearly we know $E[X]=-1$ and $Var(X)=3$ and we can compute the second moment from there.
– Siong Thye Goh
Nov 20 at 18:27
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
Ok, that clears things up. Thank you.
– Ethan
Nov 20 at 18:28
add a comment |
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