The mle algorithm for the matrix normal distribution
From MaRDI portal
Publication:4513011
DOI10.1080/00949659908811970zbMath0960.62056OpenAlexW2078537874WikidataQ56269935 ScholiaQ56269935MaRDI QIDQ4513011
Publication date: 17 May 2001
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949659908811970
stabilityexistencemaximum likelihood estimationmatrix normal distributiontwo-stage algorithmtest of model adequacyseparability of variance-covariance structure
Related Items (64)
Restricted covariance priors with applications in spatial statistics ⋮ Clustering of longitudinal interval-valued data via mixture distribution under covariance separability ⋮ MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling ⋮ Two new matrix-variate distributions with application in model-based clustering ⋮ A note on necessary and sufficient conditions of existence and uniqueness for the maximum likelihood estimator of a Kronecker-product variance-covariance matrix ⋮ Gaussian and robust Kronecker product covariance estimation: existence and uniqueness ⋮ BROWNIAN MOTION MINUS THE INDEPENDENT INCREMENTS: REPRESENTATION AND QUEUING APPLICATION ⋮ MULTI-FREQUENTIAL PERIODOGRAM ANALYSIS AND THE DETECTION OF PERIODIC COMPONENTS IN TIME SERIES ⋮ Testing variance parameters in models with a Kronecker product covariance structure ⋮ More on the Kronecker Structured Covariance Matrix ⋮ Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices ⋮ An expectation-maximization algorithm for the matrix normal distribution with an application in remote sensing ⋮ Linear models for multivariate repeated measures data with block exchangeable covariance structure ⋮ Maximum likelihood estimation for tensor normal models via castling transforms ⋮ Permutation based testing on covariance separability ⋮ Modelling students’ career indicators via mixtures of parsimonious matrix‐normal distributions ⋮ Kronecker-structured covariance models for multiway data ⋮ Visual assessment of matrix‐variate normality ⋮ Regularized matrix data clustering and its application to image analysis ⋮ Correlation Tensor Decomposition and Its Application in Spatial Imaging Data ⋮ Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data ⋮ A combinatorial algorithm for computing the entire sequence of the maximum degree of minors of a generic partitioned polynomial matrix with \(2 \times 2\) submatrices ⋮ Model selection and estimation in the matrix normal graphical model ⋮ Evaluating stationarity via change-point alternatives with applications to fMRI data ⋮ Covariance Estimation for Matrix-valued Data ⋮ Existence and uniqueness of the maximum likelihood estimator for models with a Kronecker product covariance structure ⋮ Robust tests for scatter separability beyond Gaussianity ⋮ Finite mixtures of matrix normal distributions for classifying three-way data ⋮ Limiting spectral distribution of renormalized separable sample covariance matrices when \(p/n\to 0\) ⋮ On matrix-variate regression analysis ⋮ Tensor envelope mixture model for simultaneous clustering and multiway dimension reduction ⋮ A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering ⋮ Testing the equality of matrix distributions ⋮ Empirical Bayes hierarchical models for regularizing maximum likelihood estimation in the matrix Gaussian Procrustes problem ⋮ Three skewed matrix variate distributions ⋮ Approximation with a Kronecker product structure with one component as compound symmetry or autoregression via entropy loss function ⋮ Testing a block exchangeable covariance matrix ⋮ Score tests for intercept and slope parameters of doubly multivariate linear models with skew-normal errors ⋮ Unnamed Item ⋮ Estimating and Testing a Structured Covariance Matrix for Three-Level Multivariate Data ⋮ Sparse Matrix Graphical Models ⋮ Gemini: graph estimation with matrix variate normal instances ⋮ Equivariant minimax dominators of the MLE in the array normal model ⋮ Classification of Higher-order Data with Separable Covariance and Structured Multiplicative or Additive Mean Models ⋮ The likelihood ratio test for a separable covariance matrix ⋮ On parsimonious models for modeling matrix data ⋮ A likelihood ratio test for separability of covariances ⋮ Asymptotic properties on high-dimensional multivariate regression M-estimation ⋮ Transposable regularized covariance models with an application to missing data imputation ⋮ Covariate-Adjusted Tensor Classification in High-Dimensions ⋮ Models with a Kronecker product covariance structure: estimation and testing ⋮ Covariance Weighted Procrustes Analysis ⋮ A Penalized Likelihood Method for Classification With Matrix-Valued Predictors ⋮ Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data ⋮ Existence and uniqueness of the Kronecker covariance MLE ⋮ Robust estimator of the correlation matrix with sparse Kronecker structure for a high-dimensional matrix-variate ⋮ sEparaTe ⋮ Matrix normal cluster-weighted models ⋮ Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series ⋮ Invariant Theory and Scaling Algorithms for Maximum Likelihood Estimation ⋮ Maximum Likelihood Estimation for Matrix Normal Models via Quiver Representations ⋮ Multilevel simultaneous equation model: a novel specification and estimation approach ⋮ Procrustes analysis for high-dimensional data ⋮ Matrix variate slash distribution
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Univariate repeated measures techniques applied to multivariate data
- The distribution of a generalized least squares estimator with covariance adjustment
- A note on a Manova model applied to problems in growth curve
- The mixed model for multivariate repeated measures: Validity conditions and an approximate test
- Some matrix-variate distribution theory: Notational considerations and a Bayesian application
- Statistics for Spatial Data
- A generalized multivariate analysis of variance model useful especially for growth curve problems
- Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification
- Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, II. Effects of Inequality of Variance and of Correlation Between Errors in the Two-Way Classification
This page was built for publication: The mle algorithm for the matrix normal distribution
