What is the joint density of 2 random variables that Linear combinations of the same random variables?












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Suppose we have random variable $W$ and $M$ that are independent standard normal random variables. If we were to define $X$ and $Y$ as:



$X=aW +bM$ and $Y=cW+bM$



How do we find the joint density of $X$ and $Y$? ie $f_{X,Y}$.



I found the pdf of $X$ and the pdf of $Y$ (linear combination of Normals are normal with new mean and variance) but im not sure where to go from there. I believe you can't just multiply $f_U$ and $f_V$ together because they share $W$ and $M$ which make them dependent. However, when you find $f_X$ and $f_Y$ those $M$ and $W$ terms just disappear leaving you with only $a$'s, $b$'s etc.










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    $begingroup$


    Suppose we have random variable $W$ and $M$ that are independent standard normal random variables. If we were to define $X$ and $Y$ as:



    $X=aW +bM$ and $Y=cW+bM$



    How do we find the joint density of $X$ and $Y$? ie $f_{X,Y}$.



    I found the pdf of $X$ and the pdf of $Y$ (linear combination of Normals are normal with new mean and variance) but im not sure where to go from there. I believe you can't just multiply $f_U$ and $f_V$ together because they share $W$ and $M$ which make them dependent. However, when you find $f_X$ and $f_Y$ those $M$ and $W$ terms just disappear leaving you with only $a$'s, $b$'s etc.










    share|cite|improve this question









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      0












      0








      0





      $begingroup$


      Suppose we have random variable $W$ and $M$ that are independent standard normal random variables. If we were to define $X$ and $Y$ as:



      $X=aW +bM$ and $Y=cW+bM$



      How do we find the joint density of $X$ and $Y$? ie $f_{X,Y}$.



      I found the pdf of $X$ and the pdf of $Y$ (linear combination of Normals are normal with new mean and variance) but im not sure where to go from there. I believe you can't just multiply $f_U$ and $f_V$ together because they share $W$ and $M$ which make them dependent. However, when you find $f_X$ and $f_Y$ those $M$ and $W$ terms just disappear leaving you with only $a$'s, $b$'s etc.










      share|cite|improve this question









      $endgroup$




      Suppose we have random variable $W$ and $M$ that are independent standard normal random variables. If we were to define $X$ and $Y$ as:



      $X=aW +bM$ and $Y=cW+bM$



      How do we find the joint density of $X$ and $Y$? ie $f_{X,Y}$.



      I found the pdf of $X$ and the pdf of $Y$ (linear combination of Normals are normal with new mean and variance) but im not sure where to go from there. I believe you can't just multiply $f_U$ and $f_V$ together because they share $W$ and $M$ which make them dependent. However, when you find $f_X$ and $f_Y$ those $M$ and $W$ terms just disappear leaving you with only $a$'s, $b$'s etc.







      statistics normal-distribution






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      asked Aug 24 '17 at 18:17









      RibbonSannyRibbonSanny

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          $begingroup$

          Linear combinations of jointly Gaussian random variables are also jointly Gaussian.



          Independent Gaussians are jointly Gaussian, so $(X,Y)$ follow a joint Gaussian distribution. This is specified by the $E[X],E[Y], sigma_X^2, sigma_Y^2, sigma_{XY}$.



          $E[X] = E[aW+bM] = a E[W] + b E[M] =0$ and similarly $E[Y]=0$.



          $sigma_X^2 = var(aW+bM) = a^2 var(W) + b^2 var(M) = a^2+b^2$ and similarly $sigma_Y^2 = c^2+b^2$.



          $sigma_{XY} = E[XY] - E[X]E[Y] = E[XY] - 0 = E[(aW+bM)(cW+bM)] = E[acW^2+b^2M + (ab+bc) MW]= ac+b^2 + (ab+bc)E[MW]=ac+b^2$ since $E[MW]=E[M]E[W]=0$.



          Thus, $(X,Y)$ follows a normal distribution with mean zero (vector) and covariance matrix $begin{bmatrix} sigma_X^2 & sigma_{XY} \ sigma_{XY} & sigma_Y^2 end{bmatrix}$.






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            $begingroup$

            Summarizing @Batman succinctly, let $A=begin{pmatrix}a&b\c&bend{pmatrix}$. From $begin{pmatrix}W\M end{pmatrix} sim N(0, I)$, we have



            begin{align*}
            begin{pmatrix}X\Y end{pmatrix} &= Abegin{pmatrix}W\M end{pmatrix}\
            &sim N(A0, AIA^T)\
            &= Nleft(0, begin{pmatrix}a^2+b^2 & ac+b^2 \ ac+b^2 & b^2+c^2end{pmatrix}right)
            end{align*}



            Note there is implicit assumption that $A$ is full rank, i.e. $aneq c$. To prove that independent linear combination of normal is normal, you could use the moment generating function. Linear combination of normal distribution






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              $begingroup$

              Linear combinations of jointly Gaussian random variables are also jointly Gaussian.



              Independent Gaussians are jointly Gaussian, so $(X,Y)$ follow a joint Gaussian distribution. This is specified by the $E[X],E[Y], sigma_X^2, sigma_Y^2, sigma_{XY}$.



              $E[X] = E[aW+bM] = a E[W] + b E[M] =0$ and similarly $E[Y]=0$.



              $sigma_X^2 = var(aW+bM) = a^2 var(W) + b^2 var(M) = a^2+b^2$ and similarly $sigma_Y^2 = c^2+b^2$.



              $sigma_{XY} = E[XY] - E[X]E[Y] = E[XY] - 0 = E[(aW+bM)(cW+bM)] = E[acW^2+b^2M + (ab+bc) MW]= ac+b^2 + (ab+bc)E[MW]=ac+b^2$ since $E[MW]=E[M]E[W]=0$.



              Thus, $(X,Y)$ follows a normal distribution with mean zero (vector) and covariance matrix $begin{bmatrix} sigma_X^2 & sigma_{XY} \ sigma_{XY} & sigma_Y^2 end{bmatrix}$.






              share|cite|improve this answer









              $endgroup$


















                0












                $begingroup$

                Linear combinations of jointly Gaussian random variables are also jointly Gaussian.



                Independent Gaussians are jointly Gaussian, so $(X,Y)$ follow a joint Gaussian distribution. This is specified by the $E[X],E[Y], sigma_X^2, sigma_Y^2, sigma_{XY}$.



                $E[X] = E[aW+bM] = a E[W] + b E[M] =0$ and similarly $E[Y]=0$.



                $sigma_X^2 = var(aW+bM) = a^2 var(W) + b^2 var(M) = a^2+b^2$ and similarly $sigma_Y^2 = c^2+b^2$.



                $sigma_{XY} = E[XY] - E[X]E[Y] = E[XY] - 0 = E[(aW+bM)(cW+bM)] = E[acW^2+b^2M + (ab+bc) MW]= ac+b^2 + (ab+bc)E[MW]=ac+b^2$ since $E[MW]=E[M]E[W]=0$.



                Thus, $(X,Y)$ follows a normal distribution with mean zero (vector) and covariance matrix $begin{bmatrix} sigma_X^2 & sigma_{XY} \ sigma_{XY} & sigma_Y^2 end{bmatrix}$.






                share|cite|improve this answer









                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  Linear combinations of jointly Gaussian random variables are also jointly Gaussian.



                  Independent Gaussians are jointly Gaussian, so $(X,Y)$ follow a joint Gaussian distribution. This is specified by the $E[X],E[Y], sigma_X^2, sigma_Y^2, sigma_{XY}$.



                  $E[X] = E[aW+bM] = a E[W] + b E[M] =0$ and similarly $E[Y]=0$.



                  $sigma_X^2 = var(aW+bM) = a^2 var(W) + b^2 var(M) = a^2+b^2$ and similarly $sigma_Y^2 = c^2+b^2$.



                  $sigma_{XY} = E[XY] - E[X]E[Y] = E[XY] - 0 = E[(aW+bM)(cW+bM)] = E[acW^2+b^2M + (ab+bc) MW]= ac+b^2 + (ab+bc)E[MW]=ac+b^2$ since $E[MW]=E[M]E[W]=0$.



                  Thus, $(X,Y)$ follows a normal distribution with mean zero (vector) and covariance matrix $begin{bmatrix} sigma_X^2 & sigma_{XY} \ sigma_{XY} & sigma_Y^2 end{bmatrix}$.






                  share|cite|improve this answer









                  $endgroup$



                  Linear combinations of jointly Gaussian random variables are also jointly Gaussian.



                  Independent Gaussians are jointly Gaussian, so $(X,Y)$ follow a joint Gaussian distribution. This is specified by the $E[X],E[Y], sigma_X^2, sigma_Y^2, sigma_{XY}$.



                  $E[X] = E[aW+bM] = a E[W] + b E[M] =0$ and similarly $E[Y]=0$.



                  $sigma_X^2 = var(aW+bM) = a^2 var(W) + b^2 var(M) = a^2+b^2$ and similarly $sigma_Y^2 = c^2+b^2$.



                  $sigma_{XY} = E[XY] - E[X]E[Y] = E[XY] - 0 = E[(aW+bM)(cW+bM)] = E[acW^2+b^2M + (ab+bc) MW]= ac+b^2 + (ab+bc)E[MW]=ac+b^2$ since $E[MW]=E[M]E[W]=0$.



                  Thus, $(X,Y)$ follows a normal distribution with mean zero (vector) and covariance matrix $begin{bmatrix} sigma_X^2 & sigma_{XY} \ sigma_{XY} & sigma_Y^2 end{bmatrix}$.







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                  answered Aug 24 '17 at 18:49









                  BatmanBatman

                  16.4k11634




                  16.4k11634























                      0












                      $begingroup$

                      Summarizing @Batman succinctly, let $A=begin{pmatrix}a&b\c&bend{pmatrix}$. From $begin{pmatrix}W\M end{pmatrix} sim N(0, I)$, we have



                      begin{align*}
                      begin{pmatrix}X\Y end{pmatrix} &= Abegin{pmatrix}W\M end{pmatrix}\
                      &sim N(A0, AIA^T)\
                      &= Nleft(0, begin{pmatrix}a^2+b^2 & ac+b^2 \ ac+b^2 & b^2+c^2end{pmatrix}right)
                      end{align*}



                      Note there is implicit assumption that $A$ is full rank, i.e. $aneq c$. To prove that independent linear combination of normal is normal, you could use the moment generating function. Linear combination of normal distribution






                      share|cite|improve this answer









                      $endgroup$


















                        0












                        $begingroup$

                        Summarizing @Batman succinctly, let $A=begin{pmatrix}a&b\c&bend{pmatrix}$. From $begin{pmatrix}W\M end{pmatrix} sim N(0, I)$, we have



                        begin{align*}
                        begin{pmatrix}X\Y end{pmatrix} &= Abegin{pmatrix}W\M end{pmatrix}\
                        &sim N(A0, AIA^T)\
                        &= Nleft(0, begin{pmatrix}a^2+b^2 & ac+b^2 \ ac+b^2 & b^2+c^2end{pmatrix}right)
                        end{align*}



                        Note there is implicit assumption that $A$ is full rank, i.e. $aneq c$. To prove that independent linear combination of normal is normal, you could use the moment generating function. Linear combination of normal distribution






                        share|cite|improve this answer









                        $endgroup$
















                          0












                          0








                          0





                          $begingroup$

                          Summarizing @Batman succinctly, let $A=begin{pmatrix}a&b\c&bend{pmatrix}$. From $begin{pmatrix}W\M end{pmatrix} sim N(0, I)$, we have



                          begin{align*}
                          begin{pmatrix}X\Y end{pmatrix} &= Abegin{pmatrix}W\M end{pmatrix}\
                          &sim N(A0, AIA^T)\
                          &= Nleft(0, begin{pmatrix}a^2+b^2 & ac+b^2 \ ac+b^2 & b^2+c^2end{pmatrix}right)
                          end{align*}



                          Note there is implicit assumption that $A$ is full rank, i.e. $aneq c$. To prove that independent linear combination of normal is normal, you could use the moment generating function. Linear combination of normal distribution






                          share|cite|improve this answer









                          $endgroup$



                          Summarizing @Batman succinctly, let $A=begin{pmatrix}a&b\c&bend{pmatrix}$. From $begin{pmatrix}W\M end{pmatrix} sim N(0, I)$, we have



                          begin{align*}
                          begin{pmatrix}X\Y end{pmatrix} &= Abegin{pmatrix}W\M end{pmatrix}\
                          &sim N(A0, AIA^T)\
                          &= Nleft(0, begin{pmatrix}a^2+b^2 & ac+b^2 \ ac+b^2 & b^2+c^2end{pmatrix}right)
                          end{align*}



                          Note there is implicit assumption that $A$ is full rank, i.e. $aneq c$. To prove that independent linear combination of normal is normal, you could use the moment generating function. Linear combination of normal distribution







                          share|cite|improve this answer












                          share|cite|improve this answer



                          share|cite|improve this answer










                          answered Aug 24 '17 at 19:17









                          Jirapat SamranvedhyaJirapat Samranvedhya

                          27118




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