Magnitude of Gaussian Random Projection
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Let $X= [X_1 dots X_n]$ be a $ptimes n$ random matrix, where $X_1, dots, X_n$ are i.i.d and follow $N(0,Sigma)$, and let $R$ be a random unit vector uniformly distributed on $S^{p-1}$ independently with $X$. I am interested in the distribution of $|X^top R|^2 = sum_{i=1}^n |X_i^top R|^2$.
An easy case to start with would be $Sigma = I_p$. Given any unit vector $r$, $X_i^top rsim N(0,1)$, so $|X^top R|^2sim chi^2_n$. However, this observation does not apply to general $Sigma$.
Here is another simple observation. Without loss of generality, we can always assume $Sigma$ is a diagonal matrix, since we can rotate $X_i$ and $R$ without changing the distribution of $|X^top R|^2$.
Any help would be greatly appreciated.
probability probability-distributions
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Let $X= [X_1 dots X_n]$ be a $ptimes n$ random matrix, where $X_1, dots, X_n$ are i.i.d and follow $N(0,Sigma)$, and let $R$ be a random unit vector uniformly distributed on $S^{p-1}$ independently with $X$. I am interested in the distribution of $|X^top R|^2 = sum_{i=1}^n |X_i^top R|^2$.
An easy case to start with would be $Sigma = I_p$. Given any unit vector $r$, $X_i^top rsim N(0,1)$, so $|X^top R|^2sim chi^2_n$. However, this observation does not apply to general $Sigma$.
Here is another simple observation. Without loss of generality, we can always assume $Sigma$ is a diagonal matrix, since we can rotate $X_i$ and $R$ without changing the distribution of $|X^top R|^2$.
Any help would be greatly appreciated.
probability probability-distributions
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
Let $X= [X_1 dots X_n]$ be a $ptimes n$ random matrix, where $X_1, dots, X_n$ are i.i.d and follow $N(0,Sigma)$, and let $R$ be a random unit vector uniformly distributed on $S^{p-1}$ independently with $X$. I am interested in the distribution of $|X^top R|^2 = sum_{i=1}^n |X_i^top R|^2$.
An easy case to start with would be $Sigma = I_p$. Given any unit vector $r$, $X_i^top rsim N(0,1)$, so $|X^top R|^2sim chi^2_n$. However, this observation does not apply to general $Sigma$.
Here is another simple observation. Without loss of generality, we can always assume $Sigma$ is a diagonal matrix, since we can rotate $X_i$ and $R$ without changing the distribution of $|X^top R|^2$.
Any help would be greatly appreciated.
probability probability-distributions
Let $X= [X_1 dots X_n]$ be a $ptimes n$ random matrix, where $X_1, dots, X_n$ are i.i.d and follow $N(0,Sigma)$, and let $R$ be a random unit vector uniformly distributed on $S^{p-1}$ independently with $X$. I am interested in the distribution of $|X^top R|^2 = sum_{i=1}^n |X_i^top R|^2$.
An easy case to start with would be $Sigma = I_p$. Given any unit vector $r$, $X_i^top rsim N(0,1)$, so $|X^top R|^2sim chi^2_n$. However, this observation does not apply to general $Sigma$.
Here is another simple observation. Without loss of generality, we can always assume $Sigma$ is a diagonal matrix, since we can rotate $X_i$ and $R$ without changing the distribution of $|X^top R|^2$.
Any help would be greatly appreciated.
probability probability-distributions
probability probability-distributions
asked Nov 14 at 19:51
Mayu
63
63
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