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  2. Covariance matrix - Wikipedia

    en.wikipedia.org/wiki/Covariance_matrix

    Throughout this article, boldfaced unsubscripted and are used to refer to random vectors, and Roman subscripted and are used to refer to scalar random variables.. If the entries in the column vector = (,, …,) are random variables, each with finite variance and expected value, then the covariance matrix is the matrix whose (,) entry is the covariance [1]: 177 ...

  3. Estimation of covariance matrices - Wikipedia

    en.wikipedia.org/wiki/Estimation_of_covariance...

    Simple cases, where observations are complete, can be dealt with by using the sample covariance matrix. The sample covariance matrix (SCM) is an unbiased and efficient estimator of the covariance matrix if the space of covariance matrices is viewed as an extrinsic convex cone in R p×p; however, measured using the intrinsic geometry of positive ...

  4. Bayesian vector autoregression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_vector_autoregression

    In particular, the Minnesota prior assumes that each variable follows a random walk process, possibly with drift, and therefore consists of a normal prior on a set of parameters with fixed and known covariance matrix, which will be estimated with one of three techniques: Univariate AR, Diagonal VAR, or Full VAR.

  5. Mixed-design analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Mixed-design_analysis_of...

    Sphericity of the covariance matrix: ensures the F ratios match the F distribution; For the between-subject effects to meet the assumptions of the analysis of variance, the variance for any level of a group must be the same as the variance for the mean of all other levels of the group.

  6. Covariance - Wikipedia

    en.wikipedia.org/wiki/Covariance

    A distinction must be made between (1) the covariance of two random variables, which is a population parameter that can be seen as a property of the joint probability distribution, and (2) the sample covariance, which in addition to serving as a descriptor of the sample, also serves as an estimated value of the population parameter.

  7. Variance decomposition of forecast errors - Wikipedia

    en.wikipedia.org/wiki/Variance_decomposition_of...

    is the j th column of and the subscript refers to that element of the matrix Θ i = Φ i P , {\displaystyle \Theta _{i}=\Phi _{i}P,} where P {\displaystyle P} is a lower triangular matrix obtained by a Cholesky decomposition of Σ u {\displaystyle \Sigma _{u}} such that Σ u = P P ′ {\displaystyle \Sigma _{u}=PP'} , where Σ u {\displaystyle ...

  8. Confirmatory factor analysis - Wikipedia

    en.wikipedia.org/wiki/Confirmatory_factor_analysis

    For example, if it is posited that there are two factors accounting for the covariance in the measures, and that these factors are unrelated to each other, the researcher can create a model where the correlation between factor A and factor B is constrained to zero. Model fit measures could then be obtained to assess how well the proposed model ...

  9. Genetic correlation - Wikipedia

    en.wikipedia.org/wiki/Genetic_correlation

    Given a genetic covariance matrix, the genetic correlation is computed by standardizing this, i.e., by converting the covariance matrix to a correlation matrix. Generally, if Σ {\displaystyle \Sigma } is a genetic covariance matrix and D = diag ⁡ ( Σ ) {\displaystyle D={\sqrt {\operatorname {diag} (\Sigma )}}} , then the correlation matrix ...

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