Expected value of the MLE of a binomial distribution

**noname** · September 24th 2009, 01:55 AM

Hi

I've this question:

X1,...,Xn it's a sample from a binomial distribution b(n,a). Find the maximum likelihood estimator of a and the expected value of the estimator.

I can find the MLE

Ignoring the binomial coefficient i have

$ f(x_1,...,x_n|a)=a^{\sum_{1}^{n}x_i}(1-a)^{n^2-\sum_{1}^{n}x_i} $

I take logs and differentiate to obtain:

$ \frac{d}{da}logf(x_1,...,x_n|a)=\frac{\sum_{1}^{n} x_i}{a}-\frac{n^2-\sum_{1}^{n}x_i}{1-a} $

Upon equating to zero and solving i obtain that:

$ a=\frac{\sum_{1}^{n}x_i}{n^2} $

Now i must find the expected value of a but I'm very confused on how to do this. Any hint?

Thanks

**mr fantastic** · September 24th 2009, 02:47 AM

Originally Posted by noname

Hi

I've this question:

X1,...,Xn it's a sample from a binomial distribution b(n,a). Find the maximum likelihood estimator of a and the expected value of the estimator.

I can find the MLE

Ignoring the binomial coefficient i have

$ f(x_1,...,x_n|a)=a^{\sum_{1}^{n}x_i}(1-a)^{n^2-\sum_{1}^{n}x_i} $

I take logs and differentiate to obtain:

$ \frac{d}{da}logf(x_1,...,x_n|a)=\frac{\sum_{1}^{n} x_i}{a}-\frac{n^2-\sum_{1}^{n}x_i}{1-a} $

Upon equating to zero and solving i obtain that:

$ a=\frac{\sum_{1}^{n}x_i}{n^2} $

Now i must find the expected value of a but I'm very confused on how to do this. Any hint?

Thanks

$E \left( \sum_{i = 1}^{n} x_i \right) = \sum_{i = 1}^{n} E(x_i)$ .

**noname** · September 24th 2009, 03:22 AM

Thank you!

So if I'm correct then

$ E(a)=E(\frac{\sum_{i=1}^{n}x_i}{n^2})=\frac{1}{n^2 }\sum_{i=1}^{n}E(x_i)=\frac{1}{n^2}\sum_{i=1}^{n}n a=\frac{1}{n^2}n^2a=a $

**mr fantastic** · September 24th 2009, 03:57 AM

Originally Posted by noname

Thank you!

So if I'm correct then

$ E(a)=E(\frac{\sum_{i=1}^{n}x_i}{n^2})=\frac{1}{n^2 }\sum_{i=1}^{n}E(x_i)=\frac{1}{n^2}\sum_{i=1}^{n}n a=\frac{1}{n^2}n^2a=a $

There's a difference between the two a's ....

Personally I'd use the notation $\hat{a}$ for the MLE of $a$ . Then you have $E(\hat{a}) = a$ . So the MLE of $a$ is an unbiased estimator of $a$ .

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