Difference between revisions of "Covariance matrix"
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| − | + | The matrix formed from the pairwise covariances of several random variables; more precisely, for the $ k $- | |
| + | dimensional vector $ X = ( X _ {1} \dots X _ {k} ) $ | ||
| + | the covariance matrix is the square matrix $ \Sigma = {\mathsf E} [ ( X - {\mathsf E} X ) ( X - {\mathsf E} X ) ^ {T} ] $, | ||
| + | where $ {\mathsf E} X = ( {\mathsf E} X _ {1} \dots {\mathsf E} X _ {k} ) $ | ||
| + | is the vector of mean values. The components of the covariance matrix are: | ||
| − | + | $$ | |
| + | \sigma _ {ij} = {\mathsf E} | ||
| + | [ ( X _ {i} - {\mathsf E} X _ {i} ) ( X _ {j} - {\mathsf E} X _ {j} ) ] | ||
| + | = \ | ||
| + | \mathop{\rm cov} ( X _ {i} , X _ {j} ) , | ||
| + | $$ | ||
| − | + | $$ | |
| + | i , j = 1 \dots k , | ||
| + | $$ | ||
| − | + | and for $ i = j $ | |
| + | they are the same as $ {\mathsf D} X _ {i} $( | ||
| + | $ = \mathop{\rm var} ( X _ {i} ) $) | ||
| + | (that is, the variances of the $ X _ {i} $ | ||
| + | lie on the principal diagonal). The covariance matrix is a symmetric positive semi-definite matrix. If the covariance matrix is positive definite, then the distribution of $ X $ | ||
| + | is non-degenerate; otherwise it is degenerate. For the random vector $ X $ | ||
| + | the covariance matrix plays the same role as the variance of a random variable. If the variances of the random variables $ X _ {1} \dots X _ {k} $ | ||
| + | are all equal to 1, then the covariance matrix of $ X = ( X _ {1} \dots X _ {k} ) $ | ||
| + | is the same as the [[Correlation matrix|correlation matrix]]. | ||
| − | where the | + | The sample covariance matrix for the sample $ X ^ {(} 1) \dots X ^ {(} n) $, |
| + | where the $ X ^ {(} m) $, | ||
| + | $ m = 1 \dots n $, | ||
| + | are independent and identically-distributed random $ k $- | ||
| + | dimensional vectors, consists of the variance and covariance estimators: | ||
| + | |||
| + | $$ | ||
| + | S = | ||
| + | \frac{1}{n-} | ||
| + | 1 | ||
| + | \sum _ { m= } 1 ^ { n } | ||
| + | ( X ^ {(} m) - \overline{X}\; ) ( X ^ {(} m) - \overline{X}\; ) ^ {T} , | ||
| + | $$ | ||
| + | |||
| + | where the vector $ \overline{X}\; $ | ||
| + | is the arithmetic mean of the $ X ^ {(} 1) \dots X ^ {(} n) $. | ||
| + | If the $ X ^ {(} 1) \dots X ^ {(} n) $ | ||
| + | are multivariate normally distributed with covariance matrix $ \Sigma $, | ||
| + | then $ S ( n - 1 ) / n $ | ||
| + | is the maximum-likelihood estimator of $ \Sigma $; | ||
| + | in this case the joint distribution of the elements of the matrix $ ( n - 1 ) S $ | ||
| + | is called the [[Wishart distribution|Wishart distribution]]; it is one of the fundamental distributions in multivariate statistical analysis by means of which hypotheses concerning the covariance matrix $ \Sigma $ | ||
| + | can be tested. | ||
Revision as of 17:31, 5 June 2020
The matrix formed from the pairwise covariances of several random variables; more precisely, for the $ k $-
dimensional vector $ X = ( X _ {1} \dots X _ {k} ) $
the covariance matrix is the square matrix $ \Sigma = {\mathsf E} [ ( X - {\mathsf E} X ) ( X - {\mathsf E} X ) ^ {T} ] $,
where $ {\mathsf E} X = ( {\mathsf E} X _ {1} \dots {\mathsf E} X _ {k} ) $
is the vector of mean values. The components of the covariance matrix are:
$$ \sigma _ {ij} = {\mathsf E} [ ( X _ {i} - {\mathsf E} X _ {i} ) ( X _ {j} - {\mathsf E} X _ {j} ) ] = \ \mathop{\rm cov} ( X _ {i} , X _ {j} ) , $$
$$ i , j = 1 \dots k , $$
and for $ i = j $ they are the same as $ {\mathsf D} X _ {i} $( $ = \mathop{\rm var} ( X _ {i} ) $) (that is, the variances of the $ X _ {i} $ lie on the principal diagonal). The covariance matrix is a symmetric positive semi-definite matrix. If the covariance matrix is positive definite, then the distribution of $ X $ is non-degenerate; otherwise it is degenerate. For the random vector $ X $ the covariance matrix plays the same role as the variance of a random variable. If the variances of the random variables $ X _ {1} \dots X _ {k} $ are all equal to 1, then the covariance matrix of $ X = ( X _ {1} \dots X _ {k} ) $ is the same as the correlation matrix.
The sample covariance matrix for the sample $ X ^ {(} 1) \dots X ^ {(} n) $, where the $ X ^ {(} m) $, $ m = 1 \dots n $, are independent and identically-distributed random $ k $- dimensional vectors, consists of the variance and covariance estimators:
$$ S = \frac{1}{n-} 1 \sum _ { m= } 1 ^ { n } ( X ^ {(} m) - \overline{X}\; ) ( X ^ {(} m) - \overline{X}\; ) ^ {T} , $$
where the vector $ \overline{X}\; $ is the arithmetic mean of the $ X ^ {(} 1) \dots X ^ {(} n) $. If the $ X ^ {(} 1) \dots X ^ {(} n) $ are multivariate normally distributed with covariance matrix $ \Sigma $, then $ S ( n - 1 ) / n $ is the maximum-likelihood estimator of $ \Sigma $; in this case the joint distribution of the elements of the matrix $ ( n - 1 ) S $ is called the Wishart distribution; it is one of the fundamental distributions in multivariate statistical analysis by means of which hypotheses concerning the covariance matrix $ \Sigma $ can be tested.
Covariance matrix. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Covariance_matrix&oldid=46540