Is $f(x)equiv 0$ necessary condition for $mathbbE[Xf(X)mathbb1_[0,infty)(X)]=0$?convolution of random variablesApplication of Strong Law of Large Numbers and Fubini's TheoremWhen does $mathbb E_mathbb P[X]=0$ imply $mathbb E_mathbb P[Xmidmathcal E]= 0$ $mathbb P$-a.s.$f$ convex, is $f(X)$ quasi-integrable if $X$ is?Necessary and Sufficient Conditions for Convergence to Standard NormalConditional ExpectationProblem on conditional expected value with to a random variableRelation between uncountably infinite probability space and continuous random variablesDoes there exist some probability space $(Omega,mathcal F,mathbb P)$ that admits random variables with all possible laws on $mathbb R^n$?What is the necessary and sufficient condition of Markov chain sample average converging to the expectation wrt the stationary distribution?
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Is $f(x)equiv 0$ necessary condition for $mathbbE[Xf(X)mathbb1_[0,infty)(X)]=0$?
convolution of random variablesApplication of Strong Law of Large Numbers and Fubini's TheoremWhen does $mathbb E_mathbb P[X]=0$ imply $mathbb E_mathbb P[Xmidmathcal E]= 0$ $mathbb P$-a.s.$f$ convex, is $f(X)$ quasi-integrable if $X$ is?Necessary and Sufficient Conditions for Convergence to Standard NormalConditional ExpectationProblem on conditional expected value with to a random variableRelation between uncountably infinite probability space and continuous random variablesDoes there exist some probability space $(Omega,mathcal F,mathbb P)$ that admits random variables with all possible laws on $mathbb R^n$?What is the necessary and sufficient condition of Markov chain sample average converging to the expectation wrt the stationary distribution?
$begingroup$
I have the following question:
Let $XsimmathcalN(0,1)$ and $mathbbE[Xf(X)mathbb1_[0,infty)(X)]=0$. Clearly it is sufficient that $f(x)=0$ for all values of the domain. Is it also a necessary condition?
So I separated the function $f$ into its positive and negative parts $f=f^+-f^-$. It follows that $mathbbE[Xf^+(X)mathbb1_A]>0$ for $A:=omega: g(X(omega))>0$. Now I need to show that $mathbbP(A)=0$ but I'm not sure how how to proceed. Do I need to use some sort of 0-1 law? The help would be much appreciated.
probability probability-theory probability-distributions
$endgroup$
add a comment |
$begingroup$
I have the following question:
Let $XsimmathcalN(0,1)$ and $mathbbE[Xf(X)mathbb1_[0,infty)(X)]=0$. Clearly it is sufficient that $f(x)=0$ for all values of the domain. Is it also a necessary condition?
So I separated the function $f$ into its positive and negative parts $f=f^+-f^-$. It follows that $mathbbE[Xf^+(X)mathbb1_A]>0$ for $A:=omega: g(X(omega))>0$. Now I need to show that $mathbbP(A)=0$ but I'm not sure how how to proceed. Do I need to use some sort of 0-1 law? The help would be much appreciated.
probability probability-theory probability-distributions
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$begingroup$
What is the meaning of $1_[0,infty)$. You probably mean $1_[0,infty)(X)$.
$endgroup$
– Kavi Rama Murthy
yesterday
$begingroup$
yes, that's what I mean
$endgroup$
– max
yesterday
add a comment |
$begingroup$
I have the following question:
Let $XsimmathcalN(0,1)$ and $mathbbE[Xf(X)mathbb1_[0,infty)(X)]=0$. Clearly it is sufficient that $f(x)=0$ for all values of the domain. Is it also a necessary condition?
So I separated the function $f$ into its positive and negative parts $f=f^+-f^-$. It follows that $mathbbE[Xf^+(X)mathbb1_A]>0$ for $A:=omega: g(X(omega))>0$. Now I need to show that $mathbbP(A)=0$ but I'm not sure how how to proceed. Do I need to use some sort of 0-1 law? The help would be much appreciated.
probability probability-theory probability-distributions
$endgroup$
I have the following question:
Let $XsimmathcalN(0,1)$ and $mathbbE[Xf(X)mathbb1_[0,infty)(X)]=0$. Clearly it is sufficient that $f(x)=0$ for all values of the domain. Is it also a necessary condition?
So I separated the function $f$ into its positive and negative parts $f=f^+-f^-$. It follows that $mathbbE[Xf^+(X)mathbb1_A]>0$ for $A:=omega: g(X(omega))>0$. Now I need to show that $mathbbP(A)=0$ but I'm not sure how how to proceed. Do I need to use some sort of 0-1 law? The help would be much appreciated.
probability probability-theory probability-distributions
probability probability-theory probability-distributions
edited yesterday
max
asked yesterday
maxmax
886
886
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What is the meaning of $1_[0,infty)$. You probably mean $1_[0,infty)(X)$.
$endgroup$
– Kavi Rama Murthy
yesterday
$begingroup$
yes, that's what I mean
$endgroup$
– max
yesterday
add a comment |
$begingroup$
What is the meaning of $1_[0,infty)$. You probably mean $1_[0,infty)(X)$.
$endgroup$
– Kavi Rama Murthy
yesterday
$begingroup$
yes, that's what I mean
$endgroup$
– max
yesterday
$begingroup$
What is the meaning of $1_[0,infty)$. You probably mean $1_[0,infty)(X)$.
$endgroup$
– Kavi Rama Murthy
yesterday
$begingroup$
What is the meaning of $1_[0,infty)$. You probably mean $1_[0,infty)(X)$.
$endgroup$
– Kavi Rama Murthy
yesterday
$begingroup$
yes, that's what I mean
$endgroup$
– max
yesterday
$begingroup$
yes, that's what I mean
$endgroup$
– max
yesterday
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
It does not follow that $f$ is $0$. Example: take $f(x)=a$ for $0<x<1$ and $f(x)=b$ for $1 <x<infty$. Then the hypothesis becomes $aint_0^1xphi(x)dx+bint_1^infty x phi(x)dx=0$ where $phi$ is the standard normal density function. It is clear that you can choose non-zero $a$ and $b$ satisfying this equation.
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$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
add a comment |
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1 Answer
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1 Answer
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$begingroup$
It does not follow that $f$ is $0$. Example: take $f(x)=a$ for $0<x<1$ and $f(x)=b$ for $1 <x<infty$. Then the hypothesis becomes $aint_0^1xphi(x)dx+bint_1^infty x phi(x)dx=0$ where $phi$ is the standard normal density function. It is clear that you can choose non-zero $a$ and $b$ satisfying this equation.
$endgroup$
$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
add a comment |
$begingroup$
It does not follow that $f$ is $0$. Example: take $f(x)=a$ for $0<x<1$ and $f(x)=b$ for $1 <x<infty$. Then the hypothesis becomes $aint_0^1xphi(x)dx+bint_1^infty x phi(x)dx=0$ where $phi$ is the standard normal density function. It is clear that you can choose non-zero $a$ and $b$ satisfying this equation.
$endgroup$
$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
add a comment |
$begingroup$
It does not follow that $f$ is $0$. Example: take $f(x)=a$ for $0<x<1$ and $f(x)=b$ for $1 <x<infty$. Then the hypothesis becomes $aint_0^1xphi(x)dx+bint_1^infty x phi(x)dx=0$ where $phi$ is the standard normal density function. It is clear that you can choose non-zero $a$ and $b$ satisfying this equation.
$endgroup$
It does not follow that $f$ is $0$. Example: take $f(x)=a$ for $0<x<1$ and $f(x)=b$ for $1 <x<infty$. Then the hypothesis becomes $aint_0^1xphi(x)dx+bint_1^infty x phi(x)dx=0$ where $phi$ is the standard normal density function. It is clear that you can choose non-zero $a$ and $b$ satisfying this equation.
answered yesterday
Kavi Rama MurthyKavi Rama Murthy
66k42867
66k42867
$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
add a comment |
$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
$begingroup$
Thanks for the clear explanation.
$endgroup$
– max
yesterday
add a comment |
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$begingroup$
What is the meaning of $1_[0,infty)$. You probably mean $1_[0,infty)(X)$.
$endgroup$
– Kavi Rama Murthy
yesterday
$begingroup$
yes, that's what I mean
$endgroup$
– max
yesterday