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covariance matrix (Definition)

Let $ \mathbf{X}=(X_1,\ldots,X_n)^T$ be a random vector. Then the covariance matrix of $ \mathbf{X}$, denoted by $ \mathbf{Cov(X)}$, is $ \lbrace Cov(X_i,X_j) \rbrace$. The diagonals of $ \mathbf{Cov(X)}$ are $ Cov(X_i,X_i)=Var[X_i]$. In matrix notation,

$\displaystyle \mathbf{Cov(X)}=\begin{pmatrix}Var[X_1] & \cdots & Cov(X_1,X_n) \ \vdots & & \vdots \\ Cov(X_n,X_1) & \cdots & Var[X_n] \end{pmatrix}.$

It is easily seen that $ \mathbf{Cov(X)}=\mathbf{Var[X]}$ via

$\displaystyle \begin{pmatrix}E[{X_1}^2]-E[X_1]^2 & \cdots & E[X_1X_n]-E[X_1]E[X... ...[X_n]^2 \end{pmatrix} = \mathbf{E\Big[\big(X-E[X]\big)\big(X-E[X]\big)^T\Big]}.$

The covariance matrix is symmetric and if the $ X_i$'s are independent, identically distributed (iid) with variance $ \boldsymbol{\sigma}^2$, then

$\displaystyle \mathbf{Cov(X)}=\boldsymbol{\sigma}^2\mathbf{I}.$



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Other names:  variance covariance matrix
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Cross-references: variance, iid, identically distributed, independent, symmetric, matrix, diagonals, random vector
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This is version 5 of covariance matrix, born on 2004-06-30, modified 2007-05-09.
Object id is 5975, canonical name is CovarianceMatrix.
Accessed 25101 times total.

Classification:
AMS MSC62H99 (Statistics :: Multivariate analysis :: Miscellaneous)

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