Title: Maximum Likelihood Estimation of Generalised Linear Models for Multivariate Normal Covariance Matrix
Author: Pourahmadi, M
Source: Biometrika, vol. 87, no. 2, pp. 425-435, June 2000
Descriptors: Asymptotic normality; Cholesky decomposition; Fisher information; Newton-Raphson algorithm; unconstrained parameterisation; variable selection and diagnostics
(DL)
Title: Joint Mean-Covariance Models with Applications to Longitudinal Data Unconstrained Parameterisation
Author: Pourahmadi, M
Source: Biometrika, vol. 86, no. 3, pp. 677-690, September 1999
Descriptors: Antedependence; Cholesky decomposition; Generalised linear model; Linear regression and autoregression; Link function; Multivariate normal; Nonstationary model; Stationary model
(DL)
Title: Model-based Clustering for Longitudinal Data
Author: De la Cruz-Mesia, R; Quintanab, FA; Marshall, G
Source: COMPUTATIONAL STATISTICS & DATA ANALYSIS, vol.52, no.3, pp.1441-1457, 2008
Keywords: EM algorithm; Cluster analysis; Markov chain Monte Carlo; Mixture model; Non-linear models; Random effects
(RefWorks Listed; DL)
Title: Variable Selection for Model-Based Clustering
Author: Raftery, AE; Dean, N
Source: Journal of the American Statistical Association, 101(473), 168-178, 2006
Keywords: Bayes factor; BIC; Feature selection; Model-based clustering; Unsupervised learning; Variable selection
(DL; PT)
Title: Bayes Factors
Author: KASS, RE; RAFTERY, AE
Source: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, vol.90, no.430, pp.773-795, 1995
Keywords: BAYESIAN HYPOTHESIS TESTS; BIC; IMPORTANCE SAMPLING; LAPLACE METHOD; MARKOV CHAIN MONTE CARLO; MODEL SELECTION; MONTE CARLO INTEGRATION; POSTERIOR MODEL PROBABILITIES; POSTERIOR ODDS; QUADRATURE; SCHWARZ CRITERION; SENSITIVITY ANALYSIS; STRENGTH OF EVIDENCE
(RefWorks Listed; DL)
Title: How Many Clusters? Which Clustering Method? Answers via Model-Based Cluster Analysis PRINTED
Author: Fraley, C; Raftery, AE
Source: COMPUTER JOURNAL,vol.41,no.8,pp.578-588,1998
Keywords: SPATIAL POINT-PROCESSES; EM ALGORITHM; MATHEMATICAL MORPHOLOGY; MAXIMUM-LIKELIHOOD; PRINCIPAL CURVES; FEATURES; CLASSIFICATION; CONVERGENCE; NETWORKS; MIXTURES
(RefWorks Listed; DL; PT)
Title: Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
Author: Biernacki, C; Celeux, G; Govaert, G
Source: Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 7, pp. 719 - 725, July 2000
Keywords: Mixture model, clustering, integrated likelihood, BIC, integrated completed likelihood, ICL criterion
(RefWorks Listed; DL)
Title: The EM algorithm - An Old Folk-Song Sung to a Fast New Tune
Author: Meng, XL; vanDyk, D
Source: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, vol.59, no.3, pp.511-540, 1997
Keywords: data augmentation; expectation conditional maximization algorithm; expectation conditional maximization either algorithm; Gibbs sampler; incomplete data; Markov chain Monte Carlo method; missing data; model reduction; multivariate t-distributions; Poisson model; positron emission tomography; rate of convergence; sage algorithm
(RefWorks Listed; DL)
Title: Maximum Likelihood Estimation via the ECM Algorithm: A general Framework
Author: Meng, XL; Rubin, DB
Source: BIOMETRIKA,vol.80,no.2,pp.267-278,1993
Some Key Words: Baycsian inference; Conditional maximization; Constrained optimization; EM algorithm; Gibbs sampler; Incomplete data; Iterated conditional modes; Iterative proportional fitting; Missing data
(RefWorks Listed; DL)
Title: Optimization transfer using surrogate objective functions - Rejoinder
Author: Hunter, DL; Lange, K
Source: Journal of computational and graphical statistics 9, 52-59. (2000)
(RefWorks Listed; DL)