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Sanjeena Subedi, A. Punzo, S. Ingrassia, P. McNicholas (2015)
Cluster-weighted $$t$$t-factor analyzers for robust model-based clustering and dimension reductionStatistical Methods & Applications, 24
A. Leon, K. Carriègre (2007)
General mixed‐data model: Extension of general location and grouped continuous modelsCanadian Journal of Statistics, 35
Jangsun Baek, G. McLachlan (2008)
Mixtures of Factor Analyzers with Common Factor Loadings for the Clustering and Visualisation of High-Dimensional Data
N. Gershenfeld (1997)
Nonlinear Inference and Cluster‐Weighted ModelingAnnals of the New York Academy of Sciences, 808
R. Fisher (1936)
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMSAnnals of Human Genetics, 7
H. Lopes, M. West (2004)
BAYESIAN MODEL ASSESSMENT IN FACTOR ANALYSIS
Author Wu, F. BYC., WU Jeff (1983)
ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHMAnnals of Statistics, 11
(1977)
Maximum likelihood from incomplete data via theEMalgorithm
G. Schwarz (1978)
Estimating the Dimension of a ModelAnnals of Statistics, 6
(1987)
noncompliance and missing values. Biometrics
C. Smyth, D. Coomans, Y. Everingham (2006)
Clustering noisy data in a reduced dimension space via multivariate regression treesPattern Recognit., 39
DJ Bartholomew, M Knott, I Moustaki (2011)
Latent variable models and factor analysis: a unified approach
S. Ingrassia, A. Punzo, G. Vittadini, S. Minotti (2015)
The Generalized Linear Mixed Cluster-Weighted ModelJournal of Classification, 32
J. Fonseca (2010)
On the Performance of Information Criteria in Latent Segment ModelsWorld Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 4
W. Krzanowski (1982)
Mixtures of Continuous and Categorical Variables in Discriminant Analysis: A Hypothesis-Testing ApproachBiometrics, 38
R. Browne, P. McNicholas (2012)
Model-based clustering, classification, and discriminant analysis of data with mixed typeJournal of Statistical Planning and Inference, 142
J. Anderson, J. Pemberton (1985)
The grouped continuous model for multivariate ordered categorical variables and covariate adjustment.Biometrics, 41 4
JL Schafer (1997)
Analysis of incomplete multivariate data
(1997)
Flury: data sets from flury
A. Punzo, S. Ingrassia (2016)
Clustering bivariate mixed-type data via the cluster-weighted modelComputational Statistics, 31
(1984)
Age variation in volves (Microtus californicus, M. ochrogaster) and its significance for systematic studies. Occasional papers of the Museum of Natural History
Jangsun Baek, G. McLachlan, L. Flack (2010)
Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional DataIEEE Transactions on Pattern Analysis and Machine Intelligence, 32
W. Poon, Sik-Yum Lee (1988)
Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficientsPsychometrika, 53
David Booth (1997)
Multivariate statistical inference and applications
I. Olkin, R. Tate (1961)
Multivariate Correlation Models with Mixed Discrete and Continuous VariablesAnnals of Mathematical Statistics, 32
Yahong Peng, R. Little, T. Raghunathan (2004)
An Extended General Location Model for Causal Inferences from Data Subject to Noncompliance and Missing ValuesBiometrics, 60
A. Leon, Andrea Soo, T. Williamson (2011)
Classification with discrete and continuous variables via general mixed-data modelsJournal of Applied Statistics, 38
Chuanhai Liu, D. Rubin (1998)
Ellipsoidally symmetric extensions of the general location model for mixed categorical and continuous dataBiometrika, 85
T. Belin, M. Hu, A. Young, O. Grusky (1999)
Performance of a general location model with an ignorable missing-data assumption in a multivariate mental health services study.Statistics in medicine, 18 22
P. McNicholas (2011)
On Model-Based Clustering, Classification, and Discriminant AnalysisJournal of the Iranian Statistical Society, 10
(2013)
Analysis of mixed data: methods and applications
J. Barnard, R. McCulloch, X. Meng (2000)
Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkageStatistica Sinica, 10
(1997)
Multivariate statistical analysis of human exposure to trace elements from coal in Vietnam
RA Fisher (1936)
The use of multiple measurements in taxonomic problemsAnn Eugen, 7
R. Plackett, Y. Bishop, S. Fienberg, Paul Holland (1976)
Discrete Multivariate Analysis: Theory and Practice, 139
RJ Little, DB Rubin (2002)
Statistical analysis with missing data
A Punzo, S Ingrassia (2013)
On the use of the generalized linear exponential cluster-weighted model to asses local linear independence in bivariate dataQdS J Methodol Appl Stat, 15
R. Little, M. Schluchter (1985)
Maximum likelihood estimation for mixed continuous and categorical data with missing valuesBiometrika, 72
Sanjeena Subedi, A. Punzo, S. Ingrassia, P. McNicholas (2012)
Clustering and classification via cluster-weighted factor analyzersAdvances in Data Analysis and Classification, 7
(2015)
asses local linear independence in bivariate data
Jingheng Cai, Xinyuan Song, Kwok-Hap Lam, E. Ip (2011)
A mixture of generalized latent variable models for mixed mode and heterogeneous dataComput. Stat. Data Anal., 55
General location model (GLOM) is a well-known model for analyzing mixed data. In GLOM one decomposes the joint distribution of variables into conditional distribution of continuous variables given categorical outcomes and marginal distribution of categorical variables. The first version of GLOM assumes that the covariance matrices of continuous multivariate distributions across cells, which are obtained by different combination of categorical variables, are equal. In this paper, the GLOMs are considered in both cases of equality and unequality of these covariance matrices. Three covariance structures are used across cells: the same factor analyzer, factor analyzer with unequal specific variances matrices (in the general and parsimonious forms) and factor analyzers with common factor loadings. These structures are used for both modeling covariance structure and for reducing the number of parameters. The maximum likelihood estimates of parameters are computed via the EM algorithm. As an application for these models, we investigate the classification of continuous variables within cells. Based on these models, the classification is done for usual as well as for high dimensional data sets. Finally, for showing the applicability of the proposed models for classification, results from analyzing three real data sets are presented.
Advances in Data Analysis and Classification – Springer Journals
Published: May 31, 2016
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