Maximum likelihood mean and covariance matrix estimation constrained to general positive semi-definiteness

DOI10.1080/03610928508829036zbMath0585.62092OpenAlexW1968470937MaRDI QIDQ3709658

R. H. Smith

Publication date: 1985

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928508829036

zbMATH Keywords

scoringEM algorithmcovariance matrixMaximum likelihood estimationpositive semi-definitenesslocal optimalityconvergence performanceNewton's iterationsmean and covariance matrix estimatesoptimality condition equations

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Probabilistic methods, stochastic differential equations (65C99)

Related Items (4)

First-order data sensitivity measures with applications to a multivariate signal-plus-noise problem ⋮ Effect of imprecisely known nuisance parameters on estimates of primary parameters ⋮ Weak convergence and the exponential rate of concentration for posterior density functions ⋮ An approximation for analyzing a broad class of implicitly and explicitly defined estimators

Cites Work

• Nonnegative minimum biased invariant estimation in variance component models
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• A maximum likelihood algorithm for the mean and covariance of nonidentically distributed observations
• Estimation and tests of hypotheses for the initial mean and covariance in the kalman filter model
• Linear Statistical Inference and its Applications
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