Estimation of a multivariate normal covariance matrix with staircase pattern data
DOI10.1007/s10463-006-0044-xzbMath1332.62177OpenAlexW2079690995MaRDI QIDQ995792
Publication date: 10 September 2007
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10463-006-0044-x
covariance matrixmaximum likelihood estimatorinadmissibilityreference priorJeffreys priorCholesky decompositionBartlett decompositionbest equivariant estimatorinvariant Haar measurestaircase pattern data
Estimation in multivariate analysis (62H12) Bayesian inference (62F15) Positive matrices and their generalizations; cones of matrices (15B48) Admissibility in statistical decision theory (62C15)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bartlett's decomposition of the posterior distribution of the covariance for normal monotone ignorable missing data
- Estimation of a covariance matrix under Stein's loss
- Maximum-likelihood estimation of the parameters of a multivariate normal distribution
- The variational form of certain Bayes estimators
- Efficient ML estimation of the multivariate normal distribution from incomplete data
- Estimation of a covariance matrix using the reference prior
- Noninformative priors and frequentist risks of Bayesian estimators of vector-autoregressive models
- Posterior propriety and admissibiity of hyperpriors in normal hierarchical models
- Estimation of the multivariate normal precision and covariance matrices in a star-shape model
- Maximum likelihood estimation for multivariate normal distribution with monotone sample
- Invariance, Minimax Sequential Estimation, and Continuous Time Processes
- Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
- Vec and vech operators for matrices, with some uses in jacobians and multivariate statistics
- Symmetric Matrix Derivatives with Applications
- Bayesian analysis of covariance matrices and dynamic models for longitudinal data
- Bayesian Spatial Prediction of Random Space-Time Fields With Application to Mapping PM2.5Exposure
- Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
- Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
- Inadmissibility of the maximum likelihood estimator of normal covariance matrices with the lattice conditional independence
This page was built for publication: Estimation of a multivariate normal covariance matrix with staircase pattern data