iBlog in Statistics

Title: Simultaneous Modelling of the Cholesky Decomposition of Several Covariance Matrices

Author: Pourahmadi, M; Daniels, MJ; Park, T

Source: JOURNAL OF MULTIVARIATE ANALYSIS, vol.98, no.3, pp.568-587, 2007

Descriptors: Common principal components; Longitudinal data; Maximum likelihood estimation; Missing data; Spectral decomposition; Variance-correlation decomposition

(RefWorks Listed; DL; PT)

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)