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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 ...
The Fisher information matrix is used to calculate the covariance matrices associated with maximum-likelihood estimates. It can also be used in the formulation of test statistics, such as the Wald test. In Bayesian statistics, the Fisher information plays a role in the derivation of non-informative prior distributions according to Jeffreys ...
A matrix whose entries are all either 0 or 1. Synonym for (0,1)-matrix or logical matrix. [1] Bisymmetric matrix: A square matrix that is symmetric with respect to its main diagonal and its main cross-diagonal. Block-diagonal matrix: A block matrix with entries only on the diagonal. Block matrix: A matrix partitioned in sub-matrices called blocks.
The main problem is that when the true values of the dispersion matrix are unknown, then under the null hypothesis the probability of rejecting via a test depends on the unknown dispersion matrices. [1] In practice, this dependency harms inference when the dispersion matrices are far from each other or when the sample size is not large enough ...
The matrix von Mises–Fisher distribution [7] is a distribution on the Stiefel manifold, and can be used to construct probability distributions over rotation matrices. [ 8 ] The Bingham distribution is a distribution over axes in N dimensions, or equivalently, over points on the ( N − 1)-dimensional sphere with the antipodes identified. [ 9 ]
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-definite matrices, the SCM is a biased and inefficient estimator. [1]
The disturbance in matrix A is homoscedastic; this is the simple case where OLS is the best linear unbiased estimator. The disturbances in matrices B and C are heteroscedastic. In matrix B, the variance is time-varying, increasing steadily across time; in matrix C, the variance depends on the value of . The disturbance in matrix D is ...
In probability theory and statistics, the index of dispersion, [1] dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized measure of the dispersion of a probability distribution: it is a measure used to quantify whether a set of observed occurrences are clustered or dispersed compared to a standard ...