Finding the distribution of $W$ explicitlyLimit of $(1+ x/n)^n$ when $n$ tends to infinityFinding distribution $W$Very Important question. Limiting distributionSufficient Statistics and Maximum LikelihoodFind the limiting distribution of the following random variableFnd a sequence to be convergence in distributionFinding the limiting distribution $nmin(X_1, dots , X_n)$ with uniformly distributed $X_i$Proof that this distribution is N(0,1)?Sufficient statistic for Uniform distribution.Finding distribution of $fracX_1+X_2 X_3sqrt1+X_3^2$Conditional distribution of life time for a renewal process

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Finding the distribution of $W$ explicitly


Limit of $(1+ x/n)^n$ when $n$ tends to infinityFinding distribution $W$Very Important question. Limiting distributionSufficient Statistics and Maximum LikelihoodFind the limiting distribution of the following random variableFnd a sequence to be convergence in distributionFinding the limiting distribution $nmin(X_1, dots , X_n)$ with uniformly distributed $X_i$Proof that this distribution is N(0,1)?Sufficient statistic for Uniform distribution.Finding distribution of $fracX_1+X_2 X_3sqrt1+X_3^2$Conditional distribution of life time for a renewal process













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$begingroup$



Let $X_1,ldots, X_n sim U[0, 1]$. Let $Y_n = min_1 leq ileq n X_i$.
Show that $nY_n$ converges in distribution to some random variable $W$ . Find
the distribution of $W$ explicitly.




I know that $$F(W)=P(Wle w)=P(nYnle w)=P(Ynle w/n)=1-(1-w/n)^n$$



But I cannot continue to finish the question.










share|cite|improve this question











$endgroup$
















    0












    $begingroup$



    Let $X_1,ldots, X_n sim U[0, 1]$. Let $Y_n = min_1 leq ileq n X_i$.
    Show that $nY_n$ converges in distribution to some random variable $W$ . Find
    the distribution of $W$ explicitly.




    I know that $$F(W)=P(Wle w)=P(nYnle w)=P(Ynle w/n)=1-(1-w/n)^n$$



    But I cannot continue to finish the question.










    share|cite|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$



      Let $X_1,ldots, X_n sim U[0, 1]$. Let $Y_n = min_1 leq ileq n X_i$.
      Show that $nY_n$ converges in distribution to some random variable $W$ . Find
      the distribution of $W$ explicitly.




      I know that $$F(W)=P(Wle w)=P(nYnle w)=P(Ynle w/n)=1-(1-w/n)^n$$



      But I cannot continue to finish the question.










      share|cite|improve this question











      $endgroup$





      Let $X_1,ldots, X_n sim U[0, 1]$. Let $Y_n = min_1 leq ileq n X_i$.
      Show that $nY_n$ converges in distribution to some random variable $W$ . Find
      the distribution of $W$ explicitly.




      I know that $$F(W)=P(Wle w)=P(nYnle w)=P(Ynle w/n)=1-(1-w/n)^n$$



      But I cannot continue to finish the question.







      statistics probability-distributions






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Mar 20 at 19:36









      StubbornAtom

      6,29831440




      6,29831440










      asked Mar 20 at 2:05









      aaaaaaaa

      11




      11




















          2 Answers
          2






          active

          oldest

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          0












          $begingroup$

          Hint: Use the fact that $limlimits_ntoinftyleft(1 +fracxnright)^n = e^x$ for any $xinmathbbR$.






          share|cite|improve this answer









          $endgroup$




















            0












            $begingroup$

            Slightly less elementary than the other answer, but I believe that these facts are also useful to know. I'll give the outline of the proof, necessary steps aren't that hard to do:



            1) If $ X_i sim U(0, 1)$ and iid, then $min X_i sim B(1, n)$, which is Beta random variable.



            2) It can be shown using convergence of moments that $n B(1,n) rightarrow G(1, 1)$, where $G(alpha, beta)$ is Gamma random variable.






            share|cite|improve this answer









            $endgroup$













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              2 Answers
              2






              active

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              active

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              active

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              0












              $begingroup$

              Hint: Use the fact that $limlimits_ntoinftyleft(1 +fracxnright)^n = e^x$ for any $xinmathbbR$.






              share|cite|improve this answer









              $endgroup$

















                0












                $begingroup$

                Hint: Use the fact that $limlimits_ntoinftyleft(1 +fracxnright)^n = e^x$ for any $xinmathbbR$.






                share|cite|improve this answer









                $endgroup$















                  0












                  0








                  0





                  $begingroup$

                  Hint: Use the fact that $limlimits_ntoinftyleft(1 +fracxnright)^n = e^x$ for any $xinmathbbR$.






                  share|cite|improve this answer









                  $endgroup$



                  Hint: Use the fact that $limlimits_ntoinftyleft(1 +fracxnright)^n = e^x$ for any $xinmathbbR$.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Mar 20 at 2:13









                  Minus One-TwelfthMinus One-Twelfth

                  2,933413




                  2,933413





















                      0












                      $begingroup$

                      Slightly less elementary than the other answer, but I believe that these facts are also useful to know. I'll give the outline of the proof, necessary steps aren't that hard to do:



                      1) If $ X_i sim U(0, 1)$ and iid, then $min X_i sim B(1, n)$, which is Beta random variable.



                      2) It can be shown using convergence of moments that $n B(1,n) rightarrow G(1, 1)$, where $G(alpha, beta)$ is Gamma random variable.






                      share|cite|improve this answer









                      $endgroup$

















                        0












                        $begingroup$

                        Slightly less elementary than the other answer, but I believe that these facts are also useful to know. I'll give the outline of the proof, necessary steps aren't that hard to do:



                        1) If $ X_i sim U(0, 1)$ and iid, then $min X_i sim B(1, n)$, which is Beta random variable.



                        2) It can be shown using convergence of moments that $n B(1,n) rightarrow G(1, 1)$, where $G(alpha, beta)$ is Gamma random variable.






                        share|cite|improve this answer









                        $endgroup$















                          0












                          0








                          0





                          $begingroup$

                          Slightly less elementary than the other answer, but I believe that these facts are also useful to know. I'll give the outline of the proof, necessary steps aren't that hard to do:



                          1) If $ X_i sim U(0, 1)$ and iid, then $min X_i sim B(1, n)$, which is Beta random variable.



                          2) It can be shown using convergence of moments that $n B(1,n) rightarrow G(1, 1)$, where $G(alpha, beta)$ is Gamma random variable.






                          share|cite|improve this answer









                          $endgroup$



                          Slightly less elementary than the other answer, but I believe that these facts are also useful to know. I'll give the outline of the proof, necessary steps aren't that hard to do:



                          1) If $ X_i sim U(0, 1)$ and iid, then $min X_i sim B(1, n)$, which is Beta random variable.



                          2) It can be shown using convergence of moments that $n B(1,n) rightarrow G(1, 1)$, where $G(alpha, beta)$ is Gamma random variable.







                          share|cite|improve this answer












                          share|cite|improve this answer



                          share|cite|improve this answer










                          answered Mar 21 at 6:35









                          defenestratordefenestrator

                          334




                          334



























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