Variance of random sum of random i.i.d. variables - spot the mistake?












0














Probably a trivial mistake, but can't seem to spot it:




Assume $X_1, ldots, X_tau$ and $tau in {1, ldots, n}$ are random i.i.d variables, where $S_tau = X_1 + ldots + X_tau$ denotes the random sum.



It can be shown that the following holds:



$$Var(S_tau | tau )=tau Var(X_1)$$




However, from what I know,



$$Var(S_tau | tau) = mathbb{E}( (S_tau - mathbb{E}(S_tau|tau))^2|tau) = mathbb{E}(S_tau^2|tau) - tau^2mathbb{E}(X_1)^2$$



Here $$ mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau^2$$
due to independency and identical distributions.



So with my calculations I'm getting $$Var(S_tau|tau)=tau^2 Var(X_1)$$



and I'm not sure where should the squared $tau$ disappear.



I seem to be missing something, but can't spot it.



Would be grateful for any observations!










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    0














    Probably a trivial mistake, but can't seem to spot it:




    Assume $X_1, ldots, X_tau$ and $tau in {1, ldots, n}$ are random i.i.d variables, where $S_tau = X_1 + ldots + X_tau$ denotes the random sum.



    It can be shown that the following holds:



    $$Var(S_tau | tau )=tau Var(X_1)$$




    However, from what I know,



    $$Var(S_tau | tau) = mathbb{E}( (S_tau - mathbb{E}(S_tau|tau))^2|tau) = mathbb{E}(S_tau^2|tau) - tau^2mathbb{E}(X_1)^2$$



    Here $$ mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau^2$$
    due to independency and identical distributions.



    So with my calculations I'm getting $$Var(S_tau|tau)=tau^2 Var(X_1)$$



    and I'm not sure where should the squared $tau$ disappear.



    I seem to be missing something, but can't spot it.



    Would be grateful for any observations!










    share|cite|improve this question

























      0












      0








      0







      Probably a trivial mistake, but can't seem to spot it:




      Assume $X_1, ldots, X_tau$ and $tau in {1, ldots, n}$ are random i.i.d variables, where $S_tau = X_1 + ldots + X_tau$ denotes the random sum.



      It can be shown that the following holds:



      $$Var(S_tau | tau )=tau Var(X_1)$$




      However, from what I know,



      $$Var(S_tau | tau) = mathbb{E}( (S_tau - mathbb{E}(S_tau|tau))^2|tau) = mathbb{E}(S_tau^2|tau) - tau^2mathbb{E}(X_1)^2$$



      Here $$ mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau^2$$
      due to independency and identical distributions.



      So with my calculations I'm getting $$Var(S_tau|tau)=tau^2 Var(X_1)$$



      and I'm not sure where should the squared $tau$ disappear.



      I seem to be missing something, but can't spot it.



      Would be grateful for any observations!










      share|cite|improve this question













      Probably a trivial mistake, but can't seem to spot it:




      Assume $X_1, ldots, X_tau$ and $tau in {1, ldots, n}$ are random i.i.d variables, where $S_tau = X_1 + ldots + X_tau$ denotes the random sum.



      It can be shown that the following holds:



      $$Var(S_tau | tau )=tau Var(X_1)$$




      However, from what I know,



      $$Var(S_tau | tau) = mathbb{E}( (S_tau - mathbb{E}(S_tau|tau))^2|tau) = mathbb{E}(S_tau^2|tau) - tau^2mathbb{E}(X_1)^2$$



      Here $$ mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau^2$$
      due to independency and identical distributions.



      So with my calculations I'm getting $$Var(S_tau|tau)=tau^2 Var(X_1)$$



      and I'm not sure where should the squared $tau$ disappear.



      I seem to be missing something, but can't spot it.



      Would be grateful for any observations!







      probability probability-theory conditional-expectation expected-value






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      asked 2 days ago









      Nutle

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      274110






















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          I think one oopsie is: since $X_i$ and $X_j$ are independent, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = mathbb{E}[X_1]^2$ and not $mathbb{E}[X_1^2]$! There must be another mistake, because plugging that in returns zero, but I don't see it quite yet.



          There's also a much simpler proof. $var(S_tau|tau) = var(sum_{i=1}^tau X_i|tau)$. The variance of the sum of i.i.d. random variables is the sum of the variance of any random variable in the sum, so $var(sum_{i=1}^tau X_i|tau) = sum_{i=1}^tau var(X_1)$






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            First of all, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = (mathbb{E}[X_1])^2 ne mathbb{E}[X_1^2]$



            Also, there will be $tau$ terms of $X_i^2$ and $tau(tau-1)$ terms of $X_iX_j$ in the expansion of the square. Therefore your second equation will become



            $$mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2$$



            $$Var(S_tau|tau)=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2 - tau^2mathbb{E}(X_1)^2$$



            $$= taumathbb{E}(X_1^2) -tau(mathbb{E}[X_1])^2 $$



            $$= tau(mathbb{E}(X_1^2)-(mathbb{E}[X_1])^2)$$



            $$=tau Var(X_1)$$






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              0














              The most succinct calculation writes the covariance of two iids using the Kronecker delta:$$operatorname{Var}S_tau=sum_{1le i,,jletau}operatorname{Cov}(X_i,,X_j)=sum_{ij}sigma^2delta_{ij}=tausigma^2.$$Your mistake, essentially, was to replace $delta_{ij}$ with $1$ throughout.






              share|cite|improve this answer





























                0














                Conditional on $tau=k$, where $1leq k leq n$, $S_tau=S_k=X_1+dotsc +X_k$. For any $(X_i)$ that are IID, we have that
                $$Var(X_1+dotsc+X_k)=Var(X_1)+dotsc +Var(X_k)=kVar(X_1).$$
                Thus,
                $$Var(X_1+dotsc +X_tau | tau =k)=Var(X_1 | tau =k)+dotsc Var(X_n |tau=k)$$
                $$=Var(X_1)+dotsc +Var(X_k)=k Var(X_1),$$
                and finally we obtain $Var(S_tau | tau)= tau Var(X_1)$.






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                  I think one oopsie is: since $X_i$ and $X_j$ are independent, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = mathbb{E}[X_1]^2$ and not $mathbb{E}[X_1^2]$! There must be another mistake, because plugging that in returns zero, but I don't see it quite yet.



                  There's also a much simpler proof. $var(S_tau|tau) = var(sum_{i=1}^tau X_i|tau)$. The variance of the sum of i.i.d. random variables is the sum of the variance of any random variable in the sum, so $var(sum_{i=1}^tau X_i|tau) = sum_{i=1}^tau var(X_1)$






                  share|cite|improve this answer








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                  Check out our Code of Conduct.























                    0














                    I think one oopsie is: since $X_i$ and $X_j$ are independent, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = mathbb{E}[X_1]^2$ and not $mathbb{E}[X_1^2]$! There must be another mistake, because plugging that in returns zero, but I don't see it quite yet.



                    There's also a much simpler proof. $var(S_tau|tau) = var(sum_{i=1}^tau X_i|tau)$. The variance of the sum of i.i.d. random variables is the sum of the variance of any random variable in the sum, so $var(sum_{i=1}^tau X_i|tau) = sum_{i=1}^tau var(X_1)$






                    share|cite|improve this answer








                    New contributor




                    Allen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                    Check out our Code of Conduct.





















                      0












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                      I think one oopsie is: since $X_i$ and $X_j$ are independent, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = mathbb{E}[X_1]^2$ and not $mathbb{E}[X_1^2]$! There must be another mistake, because plugging that in returns zero, but I don't see it quite yet.



                      There's also a much simpler proof. $var(S_tau|tau) = var(sum_{i=1}^tau X_i|tau)$. The variance of the sum of i.i.d. random variables is the sum of the variance of any random variable in the sum, so $var(sum_{i=1}^tau X_i|tau) = sum_{i=1}^tau var(X_1)$






                      share|cite|improve this answer








                      New contributor




                      Allen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                      Check out our Code of Conduct.









                      I think one oopsie is: since $X_i$ and $X_j$ are independent, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = mathbb{E}[X_1]^2$ and not $mathbb{E}[X_1^2]$! There must be another mistake, because plugging that in returns zero, but I don't see it quite yet.



                      There's also a much simpler proof. $var(S_tau|tau) = var(sum_{i=1}^tau X_i|tau)$. The variance of the sum of i.i.d. random variables is the sum of the variance of any random variable in the sum, so $var(sum_{i=1}^tau X_i|tau) = sum_{i=1}^tau var(X_1)$







                      share|cite|improve this answer








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                      answered 2 days ago









                      Allen

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                          0














                          First of all, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = (mathbb{E}[X_1])^2 ne mathbb{E}[X_1^2]$



                          Also, there will be $tau$ terms of $X_i^2$ and $tau(tau-1)$ terms of $X_iX_j$ in the expansion of the square. Therefore your second equation will become



                          $$mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2$$



                          $$Var(S_tau|tau)=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2 - tau^2mathbb{E}(X_1)^2$$



                          $$= taumathbb{E}(X_1^2) -tau(mathbb{E}[X_1])^2 $$



                          $$= tau(mathbb{E}(X_1^2)-(mathbb{E}[X_1])^2)$$



                          $$=tau Var(X_1)$$






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                            0














                            First of all, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = (mathbb{E}[X_1])^2 ne mathbb{E}[X_1^2]$



                            Also, there will be $tau$ terms of $X_i^2$ and $tau(tau-1)$ terms of $X_iX_j$ in the expansion of the square. Therefore your second equation will become



                            $$mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2$$



                            $$Var(S_tau|tau)=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2 - tau^2mathbb{E}(X_1)^2$$



                            $$= taumathbb{E}(X_1^2) -tau(mathbb{E}[X_1])^2 $$



                            $$= tau(mathbb{E}(X_1^2)-(mathbb{E}[X_1])^2)$$



                            $$=tau Var(X_1)$$






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                              0












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                              First of all, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = (mathbb{E}[X_1])^2 ne mathbb{E}[X_1^2]$



                              Also, there will be $tau$ terms of $X_i^2$ and $tau(tau-1)$ terms of $X_iX_j$ in the expansion of the square. Therefore your second equation will become



                              $$mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2$$



                              $$Var(S_tau|tau)=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2 - tau^2mathbb{E}(X_1)^2$$



                              $$= taumathbb{E}(X_1^2) -tau(mathbb{E}[X_1])^2 $$



                              $$= tau(mathbb{E}(X_1^2)-(mathbb{E}[X_1])^2)$$



                              $$=tau Var(X_1)$$






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                              First of all, $mathbb{E}[X_iX_j] = mathbb{E}[X_i]mathbb{E}[X_j] = (mathbb{E}[X_1])^2 ne mathbb{E}[X_1^2]$



                              Also, there will be $tau$ terms of $X_i^2$ and $tau(tau-1)$ terms of $X_iX_j$ in the expansion of the square. Therefore your second equation will become



                              $$mathbb{E}(S^2_tau|tau)=mathbb{E}((X_1+ldots+X_tau)^2|tau)=sum_{i,j}^{n}mathbb{E}(X_iX_j|tau)mathbb{1}_{{i,jleq tau}}=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2$$



                              $$Var(S_tau|tau)=mathbb{E}(X_1^2)tau + tau(tau-1)(mathbb{E}[X_1])^2 - tau^2mathbb{E}(X_1)^2$$



                              $$= taumathbb{E}(X_1^2) -tau(mathbb{E}[X_1])^2 $$



                              $$= tau(mathbb{E}(X_1^2)-(mathbb{E}[X_1])^2)$$



                              $$=tau Var(X_1)$$







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                              answered 2 days ago









                              Sauhard Sharma

                              74516




                              74516























                                  0














                                  The most succinct calculation writes the covariance of two iids using the Kronecker delta:$$operatorname{Var}S_tau=sum_{1le i,,jletau}operatorname{Cov}(X_i,,X_j)=sum_{ij}sigma^2delta_{ij}=tausigma^2.$$Your mistake, essentially, was to replace $delta_{ij}$ with $1$ throughout.






                                  share|cite|improve this answer


























                                    0














                                    The most succinct calculation writes the covariance of two iids using the Kronecker delta:$$operatorname{Var}S_tau=sum_{1le i,,jletau}operatorname{Cov}(X_i,,X_j)=sum_{ij}sigma^2delta_{ij}=tausigma^2.$$Your mistake, essentially, was to replace $delta_{ij}$ with $1$ throughout.






                                    share|cite|improve this answer
























                                      0












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                                      0






                                      The most succinct calculation writes the covariance of two iids using the Kronecker delta:$$operatorname{Var}S_tau=sum_{1le i,,jletau}operatorname{Cov}(X_i,,X_j)=sum_{ij}sigma^2delta_{ij}=tausigma^2.$$Your mistake, essentially, was to replace $delta_{ij}$ with $1$ throughout.






                                      share|cite|improve this answer












                                      The most succinct calculation writes the covariance of two iids using the Kronecker delta:$$operatorname{Var}S_tau=sum_{1le i,,jletau}operatorname{Cov}(X_i,,X_j)=sum_{ij}sigma^2delta_{ij}=tausigma^2.$$Your mistake, essentially, was to replace $delta_{ij}$ with $1$ throughout.







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                                      answered 2 days ago









                                      J.G.

                                      23.2k22137




                                      23.2k22137























                                          0














                                          Conditional on $tau=k$, where $1leq k leq n$, $S_tau=S_k=X_1+dotsc +X_k$. For any $(X_i)$ that are IID, we have that
                                          $$Var(X_1+dotsc+X_k)=Var(X_1)+dotsc +Var(X_k)=kVar(X_1).$$
                                          Thus,
                                          $$Var(X_1+dotsc +X_tau | tau =k)=Var(X_1 | tau =k)+dotsc Var(X_n |tau=k)$$
                                          $$=Var(X_1)+dotsc +Var(X_k)=k Var(X_1),$$
                                          and finally we obtain $Var(S_tau | tau)= tau Var(X_1)$.






                                          share|cite|improve this answer


























                                            0














                                            Conditional on $tau=k$, where $1leq k leq n$, $S_tau=S_k=X_1+dotsc +X_k$. For any $(X_i)$ that are IID, we have that
                                            $$Var(X_1+dotsc+X_k)=Var(X_1)+dotsc +Var(X_k)=kVar(X_1).$$
                                            Thus,
                                            $$Var(X_1+dotsc +X_tau | tau =k)=Var(X_1 | tau =k)+dotsc Var(X_n |tau=k)$$
                                            $$=Var(X_1)+dotsc +Var(X_k)=k Var(X_1),$$
                                            and finally we obtain $Var(S_tau | tau)= tau Var(X_1)$.






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                                              0












                                              0








                                              0






                                              Conditional on $tau=k$, where $1leq k leq n$, $S_tau=S_k=X_1+dotsc +X_k$. For any $(X_i)$ that are IID, we have that
                                              $$Var(X_1+dotsc+X_k)=Var(X_1)+dotsc +Var(X_k)=kVar(X_1).$$
                                              Thus,
                                              $$Var(X_1+dotsc +X_tau | tau =k)=Var(X_1 | tau =k)+dotsc Var(X_n |tau=k)$$
                                              $$=Var(X_1)+dotsc +Var(X_k)=k Var(X_1),$$
                                              and finally we obtain $Var(S_tau | tau)= tau Var(X_1)$.






                                              share|cite|improve this answer












                                              Conditional on $tau=k$, where $1leq k leq n$, $S_tau=S_k=X_1+dotsc +X_k$. For any $(X_i)$ that are IID, we have that
                                              $$Var(X_1+dotsc+X_k)=Var(X_1)+dotsc +Var(X_k)=kVar(X_1).$$
                                              Thus,
                                              $$Var(X_1+dotsc +X_tau | tau =k)=Var(X_1 | tau =k)+dotsc Var(X_n |tau=k)$$
                                              $$=Var(X_1)+dotsc +Var(X_k)=k Var(X_1),$$
                                              and finally we obtain $Var(S_tau | tau)= tau Var(X_1)$.







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                                              answered 2 days ago









                                              LoveTooNap29

                                              1,0001613




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