Using the law of iterated expectations with multiple variablesDistribution function (CDF) of the sum of two random variables + law of iterated expectationsApplication of Law of Iterated ExpectationsVersion of iterated expectations conditioned on subsets: Simple proof?Law of iterated expectations given inequalityImplications of law of iterated expectationConditional Expectation of a Product of Random Variables (Iterated Expectations)Question about Law of Iterated ExpectationsLaw of iterated expectationsWhat is wrong with this computation? Law of Iterated ExpectationsIterated expectations conditional on an event
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Using the law of iterated expectations with multiple variables
Distribution function (CDF) of the sum of two random variables + law of iterated expectationsApplication of Law of Iterated ExpectationsVersion of iterated expectations conditioned on subsets: Simple proof?Law of iterated expectations given inequalityImplications of law of iterated expectationConditional Expectation of a Product of Random Variables (Iterated Expectations)Question about Law of Iterated ExpectationsLaw of iterated expectationsWhat is wrong with this computation? Law of Iterated ExpectationsIterated expectations conditional on an event
$begingroup$
I'm given that:
$E[epsilon_i | dist_i, range_i] = 0$
How can I apply the law of iterated expectations to find that
$E[epsilon_i|dist_i] =0$
is also true?
probability conditional-expectation expected-value
$endgroup$
add a comment |
$begingroup$
I'm given that:
$E[epsilon_i | dist_i, range_i] = 0$
How can I apply the law of iterated expectations to find that
$E[epsilon_i|dist_i] =0$
is also true?
probability conditional-expectation expected-value
$endgroup$
add a comment |
$begingroup$
I'm given that:
$E[epsilon_i | dist_i, range_i] = 0$
How can I apply the law of iterated expectations to find that
$E[epsilon_i|dist_i] =0$
is also true?
probability conditional-expectation expected-value
$endgroup$
I'm given that:
$E[epsilon_i | dist_i, range_i] = 0$
How can I apply the law of iterated expectations to find that
$E[epsilon_i|dist_i] =0$
is also true?
probability conditional-expectation expected-value
probability conditional-expectation expected-value
edited Feb 27 at 2:22
mitmath514
asked Feb 27 at 1:37
mitmath514mitmath514
213
213
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Tower property
if $F_1 subset F_2$ so $E(E(Z|F_2)|F_1)=E(Z|F_1)$
by taking $F_1=sigma(X)$ and $F_2=sigma(X,Y)$ so $E(E(Z|sigma(X,Y))|sigma(X))=E(Z|sigma(X))$
on the other hand
$E(E(Z|X,Y)|X)=E(Z|X)$
choose $(Z=epsilon_i,X=dist_i,Y=range_i)$
$E[Z|X]=Ebigg(E[Z | X, Y])|X bigg) =E(0|X)=0$
(just for hint $E[Z=epsilon_i|X=dist_i]=E(E[Z=epsilon_i | X=dist_i, Y=range_i])|X=dist_i) =E(0)=0$ )
$endgroup$
add a comment |
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1 Answer
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1 Answer
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active
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active
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votes
$begingroup$
Tower property
if $F_1 subset F_2$ so $E(E(Z|F_2)|F_1)=E(Z|F_1)$
by taking $F_1=sigma(X)$ and $F_2=sigma(X,Y)$ so $E(E(Z|sigma(X,Y))|sigma(X))=E(Z|sigma(X))$
on the other hand
$E(E(Z|X,Y)|X)=E(Z|X)$
choose $(Z=epsilon_i,X=dist_i,Y=range_i)$
$E[Z|X]=Ebigg(E[Z | X, Y])|X bigg) =E(0|X)=0$
(just for hint $E[Z=epsilon_i|X=dist_i]=E(E[Z=epsilon_i | X=dist_i, Y=range_i])|X=dist_i) =E(0)=0$ )
$endgroup$
add a comment |
$begingroup$
Tower property
if $F_1 subset F_2$ so $E(E(Z|F_2)|F_1)=E(Z|F_1)$
by taking $F_1=sigma(X)$ and $F_2=sigma(X,Y)$ so $E(E(Z|sigma(X,Y))|sigma(X))=E(Z|sigma(X))$
on the other hand
$E(E(Z|X,Y)|X)=E(Z|X)$
choose $(Z=epsilon_i,X=dist_i,Y=range_i)$
$E[Z|X]=Ebigg(E[Z | X, Y])|X bigg) =E(0|X)=0$
(just for hint $E[Z=epsilon_i|X=dist_i]=E(E[Z=epsilon_i | X=dist_i, Y=range_i])|X=dist_i) =E(0)=0$ )
$endgroup$
add a comment |
$begingroup$
Tower property
if $F_1 subset F_2$ so $E(E(Z|F_2)|F_1)=E(Z|F_1)$
by taking $F_1=sigma(X)$ and $F_2=sigma(X,Y)$ so $E(E(Z|sigma(X,Y))|sigma(X))=E(Z|sigma(X))$
on the other hand
$E(E(Z|X,Y)|X)=E(Z|X)$
choose $(Z=epsilon_i,X=dist_i,Y=range_i)$
$E[Z|X]=Ebigg(E[Z | X, Y])|X bigg) =E(0|X)=0$
(just for hint $E[Z=epsilon_i|X=dist_i]=E(E[Z=epsilon_i | X=dist_i, Y=range_i])|X=dist_i) =E(0)=0$ )
$endgroup$
Tower property
if $F_1 subset F_2$ so $E(E(Z|F_2)|F_1)=E(Z|F_1)$
by taking $F_1=sigma(X)$ and $F_2=sigma(X,Y)$ so $E(E(Z|sigma(X,Y))|sigma(X))=E(Z|sigma(X))$
on the other hand
$E(E(Z|X,Y)|X)=E(Z|X)$
choose $(Z=epsilon_i,X=dist_i,Y=range_i)$
$E[Z|X]=Ebigg(E[Z | X, Y])|X bigg) =E(0|X)=0$
(just for hint $E[Z=epsilon_i|X=dist_i]=E(E[Z=epsilon_i | X=dist_i, Y=range_i])|X=dist_i) =E(0)=0$ )
answered Mar 29 at 2:10
masoudmasoud
3128
3128
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