Covariance matrix

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.