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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 Rp×p; however, measured using the intrinsic geometry of positive-definite matrices, the SCM is a biased and inefficient estimator. [1]
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 ...
where E is the expected value operator. Notably, correlation is dimensionless while covariance is in units obtained by multiplying the units of the two variables. If Y always takes on the same values as X, we have the covariance of a variable with itself (i.e. ), which is called the variance and is more commonly denoted as the square of the ...
Newey–West estimator. A Newey–West estimator is used in statistics and econometrics to provide an estimate of the covariance matrix of the parameters of a regression-type model where the standard assumptions of regression analysis do not apply. [1] It was devised by Whitney K. Newey and Kenneth D. West in 1987, although there are a number ...
Multinomial distribution. In probability theory, the multinomial distribution is a generalization of the binomial distribution. For example, it models the probability of counts for each side of a k -sided dice rolled n times. For n independent trials each of which leads to a success for exactly one of k categories, with each category having a ...
Formally, a multivariate random variable is a column vector (or its transpose, which is a row vector) whose components are random variables on the probability space , where is the sample space, is the sigma-algebra (the collection of all events), and is the probability measure (a function returning each event's probability).
Covariance. The sign of the covariance of two random variables X and Y. Covariance in probability theory and statistics is a measure of the joint variability of two random variables. [1] The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables. If greater values of one variable mainly correspond ...
Not to be confused with Covariance matrix. In probability theory and statistics, a cross-covariance matrix is a matrix whose element in the i, j position is the covariance between the i -th element of a random vector and j -th element of another random vector. A random vector is a random variable with multiple dimensions.