Distribution of Maximum Likelihood Estimator
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Why is the Maximum Likelihood Estimator Normally distributed? I can't figure out why it is true for large n in general. My attempt (for single parameter)
Let $L(theta)$ be the maximum likelihood function for the distribution $f(x;theta)$
Then after taking sample of size n
$$L(theta)=f(x_1;theta)cdot f(x_2theta)...f(x_n;theta)$$
And we want to find $theta_{max}$ such that $L(theta)$ is maximized and $theta_{max}$ is our estimate (once a sample has actually been selected)
Since $theta_{max}$ maximizes $L(theta)$ it also maximizes $ln(L(theta))$
where
$$ln(L(theta))=ln(f(x_1;theta))+ln(f(x_2;theta))...+ln(f(x_n;theta))$$
Taking the derivative with respect to $theta$
$$frac{f'(x_1;theta)}{f(x_1;theta)}+frac{f'(x_2;theta)}{f(x_2;theta)}...+frac{f'(x_n;theta)}{f(x_n;theta)}$$
$theta_{max}$ would be the solution of the above when set to 0 (after selecting values for all $x_1,x_2...x_n$) but why is it normally distributed and how do I show that it's true for large n?
probability distributions normal-distribution estimation sampling
asked 3 hours ago
Colin HicksColin Hicks
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
Why is the Maximum Likelihood Estimator Normally distributed? I can't figure out why it is true for large n in general. My attempt (for single parameter)
Let $L(theta)$ be the maximum likelihood function for the distribution $f(x;theta)$
Then after taking sample of size n
$$L(theta)=f(x_1;theta)cdot f(x_2theta)...f(x_n;theta)$$
And we want to find $theta_{max}$ such that $L(theta)$ is maximized and $theta_{max}$ is our estimate (once a sample has actually been selected)
Since $theta_{max}$ maximizes $L(theta)$ it also maximizes $ln(L(theta))$
where
$$ln(L(theta))=ln(f(x_1;theta))+ln(f(x_2;theta))...+ln(f(x_n;theta))$$
Taking the derivative with respect to $theta$
$$frac{f'(x_1;theta)}{f(x_1;theta)}+frac{f'(x_2;theta)}{f(x_2;theta)}...+frac{f'(x_n;theta)}{f(x_n;theta)}$$
$theta_{max}$ would be the solution of the above when set to 0 (after selecting values for all $x_1,x_2...x_n$) but why is it normally distributed and how do I show that it's true for large n?
probability distributions normal-distribution estimation sampling
asked 3 hours ago
Colin HicksColin Hicks
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Why is the Maximum Likelihood Estimator Normally distributed? I can't figure out why it is true for large n in general. My attempt (for single parameter)
Let $L(theta)$ be the maximum likelihood function for the distribution $f(x;theta)$
Then after taking sample of size n
$$L(theta)=f(x_1;theta)cdot f(x_2theta)...f(x_n;theta)$$
And we want to find $theta_{max}$ such that $L(theta)$ is maximized and $theta_{max}$ is our estimate (once a sample has actually been selected)
Since $theta_{max}$ maximizes $L(theta)$ it also maximizes $ln(L(theta))$
where
$$ln(L(theta))=ln(f(x_1;theta))+ln(f(x_2;theta))...+ln(f(x_n;theta))$$
Taking the derivative with respect to $theta$
$$frac{f'(x_1;theta)}{f(x_1;theta)}+frac{f'(x_2;theta)}{f(x_2;theta)}...+frac{f'(x_n;theta)}{f(x_n;theta)}$$
$theta_{max}$ would be the solution of the above when set to 0 (after selecting values for all $x_1,x_2...x_n$) but why is it normally distributed and how do I show that it's true for large n?
probability distributions normal-distribution estimation sampling
asked 3 hours ago
Colin HicksColin Hicks
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Why is the Maximum Likelihood Estimator Normally distributed? I can't figure out why it is true for large n in general. My attempt (for single parameter)
Let $L(theta)$ be the maximum likelihood function for the distribution $f(x;theta)$
Then after taking sample of size n
$$L(theta)=f(x_1;theta)cdot f(x_2theta)...f(x_n;theta)$$
And we want to find $theta_{max}$ such that $L(theta)$ is maximized and $theta_{max}$ is our estimate (once a sample has actually been selected)
Since $theta_{max}$ maximizes $L(theta)$ it also maximizes $ln(L(theta))$
where
$$ln(L(theta))=ln(f(x_1;theta))+ln(f(x_2;theta))...+ln(f(x_n;theta))$$
Taking the derivative with respect to $theta$
$$frac{f'(x_1;theta)}{f(x_1;theta)}+frac{f'(x_2;theta)}{f(x_2;theta)}...+frac{f'(x_n;theta)}{f(x_n;theta)}$$
$theta_{max}$ would be the solution of the above when set to 0 (after selecting values for all $x_1,x_2...x_n$) but why is it normally distributed and how do I show that it's true for large n?
probability distributions normal-distribution estimation sampling
probability distributions normal-distribution estimation sampling
asked 3 hours ago
Colin HicksColin Hicks
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
Colin HicksColin Hicks
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
Colin HicksColin Hicks
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
Colin HicksColin Hicks
1353
asked 3 hours ago
Colin HicksColin Hicks
1353
1353
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Colin Hicks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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MLE requires $$frac{partial ln L(theta)}{partial theta} = sum_{i=1}^n frac{ f'(x_i;theta)}{f(x_i;theta)},$$
where $f'(x_i;theta)$ could denote a gradient (allowing for the multivariate case, but still sticking to your notation). Define a new function $g(x;theta)=frac{ f'(x;theta)}{f(x;theta)}.$ Then ${g(x_i;theta)}_{i=1}^n$ is a new iid sequence of random variables, with $Eg(x_1;theta)=0$. If $Eg(x_1;theta)g(x_1;theta)'<infty$, CLT implies,
$$sqrt{n}(bar{g}_n(theta)-Eg(x_1;theta))=sqrt{n}bar{g}_n(theta) rightarrow_D N(0,E(g(x;theta)g(x;theta)'),$$
where $bar{g}_n(theta)=frac{1}{n} sum_{i=1}^n g(x_i;theta).$ The ML estimator solves the equation
$$bar{g}_n(theta)=0.$$
It follows that the ML estimator is given by
$$hat{theta}=bar{g}_n^{-1}(0).$$
So long as the set of discontinuity points of $bar{g}_n^{-1}(z)$, i.e. the set of all values of $z$ such that $bar{g}_n^{-1}(z)$ is not continuous, occur with probability zero, the continuous mapping theorem gives us asymptotic normality of $theta$.
answered 3 hours ago
dlnBdlnB
3916
New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$
$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
add a comment |
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$begingroup$
MLE requires $$frac{partial ln L(theta)}{partial theta} = sum_{i=1}^n frac{ f'(x_i;theta)}{f(x_i;theta)},$$
where $f'(x_i;theta)$ could denote a gradient (allowing for the multivariate case, but still sticking to your notation). Define a new function $g(x;theta)=frac{ f'(x;theta)}{f(x;theta)}.$ Then ${g(x_i;theta)}_{i=1}^n$ is a new iid sequence of random variables, with $Eg(x_1;theta)=0$. If $Eg(x_1;theta)g(x_1;theta)'<infty$, CLT implies,
$$sqrt{n}(bar{g}_n(theta)-Eg(x_1;theta))=sqrt{n}bar{g}_n(theta) rightarrow_D N(0,E(g(x;theta)g(x;theta)'),$$
where $bar{g}_n(theta)=frac{1}{n} sum_{i=1}^n g(x_i;theta).$ The ML estimator solves the equation
$$bar{g}_n(theta)=0.$$
It follows that the ML estimator is given by
$$hat{theta}=bar{g}_n^{-1}(0).$$
So long as the set of discontinuity points of $bar{g}_n^{-1}(z)$, i.e. the set of all values of $z$ such that $bar{g}_n^{-1}(z)$ is not continuous, occur with probability zero, the continuous mapping theorem gives us asymptotic normality of $theta$.
answered 3 hours ago
dlnBdlnB
3916
New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
add a comment |
$begingroup$
MLE requires $$frac{partial ln L(theta)}{partial theta} = sum_{i=1}^n frac{ f'(x_i;theta)}{f(x_i;theta)},$$
where $f'(x_i;theta)$ could denote a gradient (allowing for the multivariate case, but still sticking to your notation). Define a new function $g(x;theta)=frac{ f'(x;theta)}{f(x;theta)}.$ Then ${g(x_i;theta)}_{i=1}^n$ is a new iid sequence of random variables, with $Eg(x_1;theta)=0$. If $Eg(x_1;theta)g(x_1;theta)'<infty$, CLT implies,
$$sqrt{n}(bar{g}_n(theta)-Eg(x_1;theta))=sqrt{n}bar{g}_n(theta) rightarrow_D N(0,E(g(x;theta)g(x;theta)'),$$
where $bar{g}_n(theta)=frac{1}{n} sum_{i=1}^n g(x_i;theta).$ The ML estimator solves the equation
$$bar{g}_n(theta)=0.$$
It follows that the ML estimator is given by
$$hat{theta}=bar{g}_n^{-1}(0).$$
So long as the set of discontinuity points of $bar{g}_n^{-1}(z)$, i.e. the set of all values of $z$ such that $bar{g}_n^{-1}(z)$ is not continuous, occur with probability zero, the continuous mapping theorem gives us asymptotic normality of $theta$.
answered 3 hours ago
dlnBdlnB
3916
New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
add a comment |
$begingroup$
MLE requires $$frac{partial ln L(theta)}{partial theta} = sum_{i=1}^n frac{ f'(x_i;theta)}{f(x_i;theta)},$$
where $f'(x_i;theta)$ could denote a gradient (allowing for the multivariate case, but still sticking to your notation). Define a new function $g(x;theta)=frac{ f'(x;theta)}{f(x;theta)}.$ Then ${g(x_i;theta)}_{i=1}^n$ is a new iid sequence of random variables, with $Eg(x_1;theta)=0$. If $Eg(x_1;theta)g(x_1;theta)'<infty$, CLT implies,
$$sqrt{n}(bar{g}_n(theta)-Eg(x_1;theta))=sqrt{n}bar{g}_n(theta) rightarrow_D N(0,E(g(x;theta)g(x;theta)'),$$
where $bar{g}_n(theta)=frac{1}{n} sum_{i=1}^n g(x_i;theta).$ The ML estimator solves the equation
$$bar{g}_n(theta)=0.$$
It follows that the ML estimator is given by
$$hat{theta}=bar{g}_n^{-1}(0).$$
So long as the set of discontinuity points of $bar{g}_n^{-1}(z)$, i.e. the set of all values of $z$ such that $bar{g}_n^{-1}(z)$ is not continuous, occur with probability zero, the continuous mapping theorem gives us asymptotic normality of $theta$.
answered 3 hours ago
dlnBdlnB
3916
New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
MLE requires $$frac{partial ln L(theta)}{partial theta} = sum_{i=1}^n frac{ f'(x_i;theta)}{f(x_i;theta)},$$
where $f'(x_i;theta)$ could denote a gradient (allowing for the multivariate case, but still sticking to your notation). Define a new function $g(x;theta)=frac{ f'(x;theta)}{f(x;theta)}.$ Then ${g(x_i;theta)}_{i=1}^n$ is a new iid sequence of random variables, with $Eg(x_1;theta)=0$. If $Eg(x_1;theta)g(x_1;theta)'<infty$, CLT implies,
$$sqrt{n}(bar{g}_n(theta)-Eg(x_1;theta))=sqrt{n}bar{g}_n(theta) rightarrow_D N(0,E(g(x;theta)g(x;theta)'),$$
where $bar{g}_n(theta)=frac{1}{n} sum_{i=1}^n g(x_i;theta).$ The ML estimator solves the equation
$$bar{g}_n(theta)=0.$$
It follows that the ML estimator is given by
$$hat{theta}=bar{g}_n^{-1}(0).$$
So long as the set of discontinuity points of $bar{g}_n^{-1}(z)$, i.e. the set of all values of $z$ such that $bar{g}_n^{-1}(z)$ is not continuous, occur with probability zero, the continuous mapping theorem gives us asymptotic normality of $theta$.
answered 3 hours ago
dlnBdlnB
3916
New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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edited 2 hours ago
answered 3 hours ago
dlnBdlnB
3916
New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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answered 3 hours ago
dlnBdlnB
3916
answered 3 hours ago
dlnBdlnB
3916
3916
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New contributor
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
dlnB is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
add a comment |
$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
$frac{f'(x)}{f(x)}$ is just another function of x so central limit theorem applies thank you for that
$endgroup$
– Colin Hicks
3 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
$begingroup$
You're welcome :)
$endgroup$
– dlnB
2 hours ago
add a comment |
Colin Hicks is a new contributor. Be nice, and check out our Code of Conduct.
Colin Hicks is a new contributor. Be nice, and check out our Code of Conduct.
Colin Hicks is a new contributor. Be nice, and check out our Code of Conduct.
Colin Hicks is a new contributor. Be nice, and check out our Code of Conduct.
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