Matrix calculus for statistics
up vote
2
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I want to calculate the variance of a sum of random variables $ (X_1+X_2) $.
Doing statistics, I have to learn maths starting from the end and it is quite difficult, yet very interesting (please consider that I only have the very basic skills in maths).
For now I am doing this calculus manually with the formula $ mathrm{var}(X_1+X_2) = mathrm{var}(X_1) + mathrm{var}(X_2) + 2mathrm{cov}(X_1,X_2)$.
But I am now facing much larger sums (with some minus) and being able to do so with matrix calculation would save me a lot of time (and would be very satisfying too).
I searched every resource in matrix calculus but couldn't find anything usable with my knowledge.
How can I do this calculus from the variance-covariance matrix
$$
begin{pmatrix}
mathrm{var}(X_1) & mathrm{cov}(X_1,X_2) \
mathrm{cov}(X_1,X_2) & mathrm{var}(X_2) \
end{pmatrix}
$$
[preferentially extended to substractions and n terms, like $ (X_1+X_2-X_3) $]?
NB: this is not a statistic question and doesn't belong to stats.stackexchange. I want to understand the thought process of turning scalar calculation to matrix.
matrices descriptive-statistics
New contributor
Dan Chaltiel 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 |
up vote
2
down vote
favorite
I want to calculate the variance of a sum of random variables $ (X_1+X_2) $.
Doing statistics, I have to learn maths starting from the end and it is quite difficult, yet very interesting (please consider that I only have the very basic skills in maths).
For now I am doing this calculus manually with the formula $ mathrm{var}(X_1+X_2) = mathrm{var}(X_1) + mathrm{var}(X_2) + 2mathrm{cov}(X_1,X_2)$.
But I am now facing much larger sums (with some minus) and being able to do so with matrix calculation would save me a lot of time (and would be very satisfying too).
I searched every resource in matrix calculus but couldn't find anything usable with my knowledge.
How can I do this calculus from the variance-covariance matrix
$$
begin{pmatrix}
mathrm{var}(X_1) & mathrm{cov}(X_1,X_2) \
mathrm{cov}(X_1,X_2) & mathrm{var}(X_2) \
end{pmatrix}
$$
[preferentially extended to substractions and n terms, like $ (X_1+X_2-X_3) $]?
NB: this is not a statistic question and doesn't belong to stats.stackexchange. I want to understand the thought process of turning scalar calculation to matrix.
matrices descriptive-statistics
New contributor
Dan Chaltiel 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 |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I want to calculate the variance of a sum of random variables $ (X_1+X_2) $.
Doing statistics, I have to learn maths starting from the end and it is quite difficult, yet very interesting (please consider that I only have the very basic skills in maths).
For now I am doing this calculus manually with the formula $ mathrm{var}(X_1+X_2) = mathrm{var}(X_1) + mathrm{var}(X_2) + 2mathrm{cov}(X_1,X_2)$.
But I am now facing much larger sums (with some minus) and being able to do so with matrix calculation would save me a lot of time (and would be very satisfying too).
I searched every resource in matrix calculus but couldn't find anything usable with my knowledge.
How can I do this calculus from the variance-covariance matrix
$$
begin{pmatrix}
mathrm{var}(X_1) & mathrm{cov}(X_1,X_2) \
mathrm{cov}(X_1,X_2) & mathrm{var}(X_2) \
end{pmatrix}
$$
[preferentially extended to substractions and n terms, like $ (X_1+X_2-X_3) $]?
NB: this is not a statistic question and doesn't belong to stats.stackexchange. I want to understand the thought process of turning scalar calculation to matrix.
matrices descriptive-statistics
New contributor
Dan Chaltiel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I want to calculate the variance of a sum of random variables $ (X_1+X_2) $.
Doing statistics, I have to learn maths starting from the end and it is quite difficult, yet very interesting (please consider that I only have the very basic skills in maths).
For now I am doing this calculus manually with the formula $ mathrm{var}(X_1+X_2) = mathrm{var}(X_1) + mathrm{var}(X_2) + 2mathrm{cov}(X_1,X_2)$.
But I am now facing much larger sums (with some minus) and being able to do so with matrix calculation would save me a lot of time (and would be very satisfying too).
I searched every resource in matrix calculus but couldn't find anything usable with my knowledge.
How can I do this calculus from the variance-covariance matrix
$$
begin{pmatrix}
mathrm{var}(X_1) & mathrm{cov}(X_1,X_2) \
mathrm{cov}(X_1,X_2) & mathrm{var}(X_2) \
end{pmatrix}
$$
[preferentially extended to substractions and n terms, like $ (X_1+X_2-X_3) $]?
NB: this is not a statistic question and doesn't belong to stats.stackexchange. I want to understand the thought process of turning scalar calculation to matrix.
matrices descriptive-statistics
matrices descriptive-statistics
New contributor
Dan Chaltiel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Dan Chaltiel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Jean-Claude Arbaut
14.6k63362
14.6k63362
New contributor
Dan Chaltiel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Dan Chaltiel
1134
1134
New contributor
Dan Chaltiel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Dan Chaltiel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Dan Chaltiel 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 |
2 Answers
2
active
oldest
votes
up vote
3
down vote
accepted
The variance-covariance matrix of $X$ is $frac1n(X-bar X)^T(X-bar X)$.
Now, you want to compute the variance of the vector $u=Xbeta$ for some vector $beta$. This variance is
$$Var(u)=frac1n(u-bar u)^T(u-bar u)=frac1n(Xbeta-bar Xbeta)^T(Xbeta-bar Xbeta)\
=frac1nbeta^T(X-bar X)^T(X-bar X)beta=beta^TVar(X)beta$$
+1 Elegant solution
– caverac
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
add a comment |
up vote
2
down vote
The key point here is that
$$
mathbb{V}{rm ar}[X] = mathbb{C}{rm ov}[X, X]
$$
so that you can express your first expression as
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + a_2 X_2] &=& a_1^2mathbb{V}{rm ar}[X_1] + a_2^2mathbb{V}{rm ar}[X_2] + 2 a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + 2a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + a_1a_2 mathbb{C}{rm ov}[X_1, X_2] + a_2a_1 mathbb{C}{rm ov}[X_2, X_1] \
&=& sum_{i=1}^2 sum_{j=1}^2 a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
In general
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + cdots a_n X_n] &=& sum_{i=1}^n sum_{j=1}^n a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
1
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
3
down vote
accepted
The variance-covariance matrix of $X$ is $frac1n(X-bar X)^T(X-bar X)$.
Now, you want to compute the variance of the vector $u=Xbeta$ for some vector $beta$. This variance is
$$Var(u)=frac1n(u-bar u)^T(u-bar u)=frac1n(Xbeta-bar Xbeta)^T(Xbeta-bar Xbeta)\
=frac1nbeta^T(X-bar X)^T(X-bar X)beta=beta^TVar(X)beta$$
+1 Elegant solution
– caverac
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
add a comment |
up vote
3
down vote
accepted
The variance-covariance matrix of $X$ is $frac1n(X-bar X)^T(X-bar X)$.
Now, you want to compute the variance of the vector $u=Xbeta$ for some vector $beta$. This variance is
$$Var(u)=frac1n(u-bar u)^T(u-bar u)=frac1n(Xbeta-bar Xbeta)^T(Xbeta-bar Xbeta)\
=frac1nbeta^T(X-bar X)^T(X-bar X)beta=beta^TVar(X)beta$$
+1 Elegant solution
– caverac
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
add a comment |
up vote
3
down vote
accepted
up vote
3
down vote
accepted
The variance-covariance matrix of $X$ is $frac1n(X-bar X)^T(X-bar X)$.
Now, you want to compute the variance of the vector $u=Xbeta$ for some vector $beta$. This variance is
$$Var(u)=frac1n(u-bar u)^T(u-bar u)=frac1n(Xbeta-bar Xbeta)^T(Xbeta-bar Xbeta)\
=frac1nbeta^T(X-bar X)^T(X-bar X)beta=beta^TVar(X)beta$$
The variance-covariance matrix of $X$ is $frac1n(X-bar X)^T(X-bar X)$.
Now, you want to compute the variance of the vector $u=Xbeta$ for some vector $beta$. This variance is
$$Var(u)=frac1n(u-bar u)^T(u-bar u)=frac1n(Xbeta-bar Xbeta)^T(Xbeta-bar Xbeta)\
=frac1nbeta^T(X-bar X)^T(X-bar X)beta=beta^TVar(X)beta$$
edited yesterday
answered yesterday
Jean-Claude Arbaut
14.6k63362
14.6k63362
+1 Elegant solution
– caverac
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
add a comment |
+1 Elegant solution
– caverac
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
+1 Elegant solution
– caverac
yesterday
+1 Elegant solution
– caverac
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
Great answer, thanks ! Learned a lot with this !
– Dan Chaltiel
yesterday
add a comment |
up vote
2
down vote
The key point here is that
$$
mathbb{V}{rm ar}[X] = mathbb{C}{rm ov}[X, X]
$$
so that you can express your first expression as
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + a_2 X_2] &=& a_1^2mathbb{V}{rm ar}[X_1] + a_2^2mathbb{V}{rm ar}[X_2] + 2 a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + 2a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + a_1a_2 mathbb{C}{rm ov}[X_1, X_2] + a_2a_1 mathbb{C}{rm ov}[X_2, X_1] \
&=& sum_{i=1}^2 sum_{j=1}^2 a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
In general
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + cdots a_n X_n] &=& sum_{i=1}^n sum_{j=1}^n a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
1
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
add a comment |
up vote
2
down vote
The key point here is that
$$
mathbb{V}{rm ar}[X] = mathbb{C}{rm ov}[X, X]
$$
so that you can express your first expression as
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + a_2 X_2] &=& a_1^2mathbb{V}{rm ar}[X_1] + a_2^2mathbb{V}{rm ar}[X_2] + 2 a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + 2a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + a_1a_2 mathbb{C}{rm ov}[X_1, X_2] + a_2a_1 mathbb{C}{rm ov}[X_2, X_1] \
&=& sum_{i=1}^2 sum_{j=1}^2 a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
In general
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + cdots a_n X_n] &=& sum_{i=1}^n sum_{j=1}^n a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
1
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
add a comment |
up vote
2
down vote
up vote
2
down vote
The key point here is that
$$
mathbb{V}{rm ar}[X] = mathbb{C}{rm ov}[X, X]
$$
so that you can express your first expression as
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + a_2 X_2] &=& a_1^2mathbb{V}{rm ar}[X_1] + a_2^2mathbb{V}{rm ar}[X_2] + 2 a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + 2a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + a_1a_2 mathbb{C}{rm ov}[X_1, X_2] + a_2a_1 mathbb{C}{rm ov}[X_2, X_1] \
&=& sum_{i=1}^2 sum_{j=1}^2 a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
In general
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + cdots a_n X_n] &=& sum_{i=1}^n sum_{j=1}^n a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
The key point here is that
$$
mathbb{V}{rm ar}[X] = mathbb{C}{rm ov}[X, X]
$$
so that you can express your first expression as
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + a_2 X_2] &=& a_1^2mathbb{V}{rm ar}[X_1] + a_2^2mathbb{V}{rm ar}[X_2] + 2 a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + 2a_1a_2 mathbb{C}{rm ov}[X_1, X_2] \
&=& a_1^2 mathbb{C}{rm ov}[X_1, X_1] + a_2^2 mathbb{C}{rm ov}[X_2, X_2] + a_1a_2 mathbb{C}{rm ov}[X_1, X_2] + a_2a_1 mathbb{C}{rm ov}[X_2, X_1] \
&=& sum_{i=1}^2 sum_{j=1}^2 a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
In general
begin{eqnarray}
mathbb{V}{rm ar}[a_1 X_1 + cdots a_n X_n] &=& sum_{i=1}^n sum_{j=1}^n a_i a_j mathbb{C}{rm ov}[X_i, X_j]
end{eqnarray}
answered yesterday
caverac
11.2k21027
11.2k21027
1
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
add a comment |
1
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
1
1
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
+1, but I think the OP wanted to see how this is done with matrices, that is how to write it as $a^TVar(X)a$.
– Jean-Claude Arbaut
yesterday
add a comment |
Dan Chaltiel is a new contributor. Be nice, and check out our Code of Conduct.
Dan Chaltiel is a new contributor. Be nice, and check out our Code of Conduct.
Dan Chaltiel is a new contributor. Be nice, and check out our Code of Conduct.
Dan Chaltiel is a new contributor. Be nice, and check out our Code of Conduct.
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