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Expectation using survival function formula?



The 2019 Stack Overflow Developer Survey Results Are InSum of truncated normalsExpectation and Variance of Conditional Sum (using formal definition of conditional expectation)Expectation of a non-negative random variableRandom variable with infinite expectation but finite conditional expectationSimplified Strong Law of Large Number by Using Truncating FunctionSequence of random variables with infinite expectation, but partial sum convergesProof that a Gamma distribution is finite almost surelyFinite expectation induces finite almost surelySupremum of the sample mean without first moment assumption.Upper bound of expected maximum of weighted sub-gaussian r.v.s












0












$begingroup$


I think my textbook has an error in the worked solutions, but I want to double check I just don't misunderstand.



Let $X_i$ be a random variable such that $P(X_i geq x ) = e/xlog x$ for $x geq e$



We know that if $X geq 0$ then $E[X] = int_0^infty P(Xgeq x)dx$



Now, the textbook gives the answer as



$$E[X] = int_e^infty e/xlog x space dx$$



However, surely using the above formula it should actually be



$$E[X] = e+ int_e^infty e/xlog x space dx$$



Since $P(X > y) = 1$ for $y in [0,e]$?



Or am I misunderstanding something basic here?



Edit:



The fact $E[X]$ is infinite doesn't matter, as the question does the same for the truncated variables $E[X 1_{(|X|leq n)}]$










share|cite|improve this question











$endgroup$








  • 2




    $begingroup$
    Where does the summand $e$ in your calculation come from?
    $endgroup$
    – J. Wang
    Mar 21 at 15:01










  • $begingroup$
    What is the name of the survival function?
    $endgroup$
    – callculus
    Mar 21 at 16:09










  • $begingroup$
    $E(X)$ does not converge. See here
    $endgroup$
    – callculus
    Mar 21 at 16:35










  • $begingroup$
    I know it does not converge, But supposing for a moment it did.... Since this working does the same for the truncated variable (which does converge)
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00










  • $begingroup$
    @J.Wang from integrating $P(X > y)=1$ from $0$ to $e$
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00


















0












$begingroup$


I think my textbook has an error in the worked solutions, but I want to double check I just don't misunderstand.



Let $X_i$ be a random variable such that $P(X_i geq x ) = e/xlog x$ for $x geq e$



We know that if $X geq 0$ then $E[X] = int_0^infty P(Xgeq x)dx$



Now, the textbook gives the answer as



$$E[X] = int_e^infty e/xlog x space dx$$



However, surely using the above formula it should actually be



$$E[X] = e+ int_e^infty e/xlog x space dx$$



Since $P(X > y) = 1$ for $y in [0,e]$?



Or am I misunderstanding something basic here?



Edit:



The fact $E[X]$ is infinite doesn't matter, as the question does the same for the truncated variables $E[X 1_{(|X|leq n)}]$










share|cite|improve this question











$endgroup$








  • 2




    $begingroup$
    Where does the summand $e$ in your calculation come from?
    $endgroup$
    – J. Wang
    Mar 21 at 15:01










  • $begingroup$
    What is the name of the survival function?
    $endgroup$
    – callculus
    Mar 21 at 16:09










  • $begingroup$
    $E(X)$ does not converge. See here
    $endgroup$
    – callculus
    Mar 21 at 16:35










  • $begingroup$
    I know it does not converge, But supposing for a moment it did.... Since this working does the same for the truncated variable (which does converge)
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00










  • $begingroup$
    @J.Wang from integrating $P(X > y)=1$ from $0$ to $e$
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00
















0












0








0





$begingroup$


I think my textbook has an error in the worked solutions, but I want to double check I just don't misunderstand.



Let $X_i$ be a random variable such that $P(X_i geq x ) = e/xlog x$ for $x geq e$



We know that if $X geq 0$ then $E[X] = int_0^infty P(Xgeq x)dx$



Now, the textbook gives the answer as



$$E[X] = int_e^infty e/xlog x space dx$$



However, surely using the above formula it should actually be



$$E[X] = e+ int_e^infty e/xlog x space dx$$



Since $P(X > y) = 1$ for $y in [0,e]$?



Or am I misunderstanding something basic here?



Edit:



The fact $E[X]$ is infinite doesn't matter, as the question does the same for the truncated variables $E[X 1_{(|X|leq n)}]$










share|cite|improve this question











$endgroup$




I think my textbook has an error in the worked solutions, but I want to double check I just don't misunderstand.



Let $X_i$ be a random variable such that $P(X_i geq x ) = e/xlog x$ for $x geq e$



We know that if $X geq 0$ then $E[X] = int_0^infty P(Xgeq x)dx$



Now, the textbook gives the answer as



$$E[X] = int_e^infty e/xlog x space dx$$



However, surely using the above formula it should actually be



$$E[X] = e+ int_e^infty e/xlog x space dx$$



Since $P(X > y) = 1$ for $y in [0,e]$?



Or am I misunderstanding something basic here?



Edit:



The fact $E[X]$ is infinite doesn't matter, as the question does the same for the truncated variables $E[X 1_{(|X|leq n)}]$







probability






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Mar 22 at 1:01







Xiaomi

















asked Mar 21 at 14:48









XiaomiXiaomi

1,081115




1,081115








  • 2




    $begingroup$
    Where does the summand $e$ in your calculation come from?
    $endgroup$
    – J. Wang
    Mar 21 at 15:01










  • $begingroup$
    What is the name of the survival function?
    $endgroup$
    – callculus
    Mar 21 at 16:09










  • $begingroup$
    $E(X)$ does not converge. See here
    $endgroup$
    – callculus
    Mar 21 at 16:35










  • $begingroup$
    I know it does not converge, But supposing for a moment it did.... Since this working does the same for the truncated variable (which does converge)
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00










  • $begingroup$
    @J.Wang from integrating $P(X > y)=1$ from $0$ to $e$
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00
















  • 2




    $begingroup$
    Where does the summand $e$ in your calculation come from?
    $endgroup$
    – J. Wang
    Mar 21 at 15:01










  • $begingroup$
    What is the name of the survival function?
    $endgroup$
    – callculus
    Mar 21 at 16:09










  • $begingroup$
    $E(X)$ does not converge. See here
    $endgroup$
    – callculus
    Mar 21 at 16:35










  • $begingroup$
    I know it does not converge, But supposing for a moment it did.... Since this working does the same for the truncated variable (which does converge)
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00










  • $begingroup$
    @J.Wang from integrating $P(X > y)=1$ from $0$ to $e$
    $endgroup$
    – Xiaomi
    Mar 22 at 1:00










2




2




$begingroup$
Where does the summand $e$ in your calculation come from?
$endgroup$
– J. Wang
Mar 21 at 15:01




$begingroup$
Where does the summand $e$ in your calculation come from?
$endgroup$
– J. Wang
Mar 21 at 15:01












$begingroup$
What is the name of the survival function?
$endgroup$
– callculus
Mar 21 at 16:09




$begingroup$
What is the name of the survival function?
$endgroup$
– callculus
Mar 21 at 16:09












$begingroup$
$E(X)$ does not converge. See here
$endgroup$
– callculus
Mar 21 at 16:35




$begingroup$
$E(X)$ does not converge. See here
$endgroup$
– callculus
Mar 21 at 16:35












$begingroup$
I know it does not converge, But supposing for a moment it did.... Since this working does the same for the truncated variable (which does converge)
$endgroup$
– Xiaomi
Mar 22 at 1:00




$begingroup$
I know it does not converge, But supposing for a moment it did.... Since this working does the same for the truncated variable (which does converge)
$endgroup$
– Xiaomi
Mar 22 at 1:00












$begingroup$
@J.Wang from integrating $P(X > y)=1$ from $0$ to $e$
$endgroup$
– Xiaomi
Mar 22 at 1:00






$begingroup$
@J.Wang from integrating $P(X > y)=1$ from $0$ to $e$
$endgroup$
– Xiaomi
Mar 22 at 1:00












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