Estimation of Covariance Matrices with Linear Structure and Moving Average Processes of Finite Order
STANFORD UNIV CA DEPT OF STATISTICS
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One or more observations are made on a random vector, whose coveriance matrix may be a linear combination of known symmetric matrices and whose mean vector may be a linear combination of known vectors the coefficients of the linear combinations are unknown parameters to be estimated. Under the assumption of normality equations are developed for the maximum likelihood estimates. The solution of thes equations by iterative methods is indicated. A sequence of observations on a moving-average process of finite order may be considered as an observation on a random vector whose covariance matrix has the linear structure. The general method of estimation applied to this problem is particularly easy to compute.
- Statistics and Probability