MLE derivation for RV that follows Binomial distribution












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Looking at Cambridge stats notes (page 10). They have that $X sim B(n,p)$ and they show that the log likelihood of the data is maximized when $frac{x}{hat{p}}-frac{n-x}{1-hat{p}} = 0$. But when I derive this, I have a plus in there:



$$text{log likelihood}(p) = log f(x|p) = text{constant} + x log{p} + (n-x) log{1-p}$$



When you take a derivative of that with respect to $p$, you get:



$$frac{x}{p} + frac{n-x}{1-p}$$



What's wrong here?










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  • $begingroup$
    ok I see... (1-p) in the third term..
    $endgroup$
    – i squared - Keep it Real
    Dec 14 '18 at 11:24
















0












$begingroup$


Looking at Cambridge stats notes (page 10). They have that $X sim B(n,p)$ and they show that the log likelihood of the data is maximized when $frac{x}{hat{p}}-frac{n-x}{1-hat{p}} = 0$. But when I derive this, I have a plus in there:



$$text{log likelihood}(p) = log f(x|p) = text{constant} + x log{p} + (n-x) log{1-p}$$



When you take a derivative of that with respect to $p$, you get:



$$frac{x}{p} + frac{n-x}{1-p}$$



What's wrong here?










share|cite|improve this question









$endgroup$












  • $begingroup$
    ok I see... (1-p) in the third term..
    $endgroup$
    – i squared - Keep it Real
    Dec 14 '18 at 11:24














0












0








0





$begingroup$


Looking at Cambridge stats notes (page 10). They have that $X sim B(n,p)$ and they show that the log likelihood of the data is maximized when $frac{x}{hat{p}}-frac{n-x}{1-hat{p}} = 0$. But when I derive this, I have a plus in there:



$$text{log likelihood}(p) = log f(x|p) = text{constant} + x log{p} + (n-x) log{1-p}$$



When you take a derivative of that with respect to $p$, you get:



$$frac{x}{p} + frac{n-x}{1-p}$$



What's wrong here?










share|cite|improve this question









$endgroup$




Looking at Cambridge stats notes (page 10). They have that $X sim B(n,p)$ and they show that the log likelihood of the data is maximized when $frac{x}{hat{p}}-frac{n-x}{1-hat{p}} = 0$. But when I derive this, I have a plus in there:



$$text{log likelihood}(p) = log f(x|p) = text{constant} + x log{p} + (n-x) log{1-p}$$



When you take a derivative of that with respect to $p$, you get:



$$frac{x}{p} + frac{n-x}{1-p}$$



What's wrong here?







statistics






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share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked Dec 14 '18 at 11:22









i squared - Keep it Reali squared - Keep it Real

1,5851925




1,5851925












  • $begingroup$
    ok I see... (1-p) in the third term..
    $endgroup$
    – i squared - Keep it Real
    Dec 14 '18 at 11:24


















  • $begingroup$
    ok I see... (1-p) in the third term..
    $endgroup$
    – i squared - Keep it Real
    Dec 14 '18 at 11:24
















$begingroup$
ok I see... (1-p) in the third term..
$endgroup$
– i squared - Keep it Real
Dec 14 '18 at 11:24




$begingroup$
ok I see... (1-p) in the third term..
$endgroup$
– i squared - Keep it Real
Dec 14 '18 at 11:24










1 Answer
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$begingroup$

As we differentiate, we use chain rule



$$frac{d}{dp}(xlog p + (n-x) log (1-p))= frac{x}{p}+frac{n-x}{1-p}frac{d}{dp}(1-p)=frac{x}{p}-frac{n-x}{1-p}$$






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    1 Answer
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    1 Answer
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    $begingroup$

    As we differentiate, we use chain rule



    $$frac{d}{dp}(xlog p + (n-x) log (1-p))= frac{x}{p}+frac{n-x}{1-p}frac{d}{dp}(1-p)=frac{x}{p}-frac{n-x}{1-p}$$






    share|cite|improve this answer









    $endgroup$


















      0












      $begingroup$

      As we differentiate, we use chain rule



      $$frac{d}{dp}(xlog p + (n-x) log (1-p))= frac{x}{p}+frac{n-x}{1-p}frac{d}{dp}(1-p)=frac{x}{p}-frac{n-x}{1-p}$$






      share|cite|improve this answer









      $endgroup$
















        0












        0








        0





        $begingroup$

        As we differentiate, we use chain rule



        $$frac{d}{dp}(xlog p + (n-x) log (1-p))= frac{x}{p}+frac{n-x}{1-p}frac{d}{dp}(1-p)=frac{x}{p}-frac{n-x}{1-p}$$






        share|cite|improve this answer









        $endgroup$



        As we differentiate, we use chain rule



        $$frac{d}{dp}(xlog p + (n-x) log (1-p))= frac{x}{p}+frac{n-x}{1-p}frac{d}{dp}(1-p)=frac{x}{p}-frac{n-x}{1-p}$$







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Dec 14 '18 at 11:57









        Siong Thye GohSiong Thye Goh

        100k1465117




        100k1465117






























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