Access the full text.
Sign up today, get DeepDyve free for 14 days.
Lawrence Laboratory, Lindsay Schachinger (1994)Summary of the
D. Hand, Keming Yu (2001)Idiot's Bayes—Not So Stupid After All?
International Statistical Review, 69
M. Meilă, Michael Jordan (2001)Learning with Mixtures of Trees
J. Mach. Learn. Res., 1
S. Handelman, D. Leverett, M. Espeland, J. Curzon (1986)Clinical radiographic evaluation of sealed carious and sound tooth surfaces.
Journal of the American Dental Association, 113 5
(1997)The EM Algorithm, Wiley Series in Probability and Statistics: Applied Probability and Statistics, New York: Wiley-Interscience
J. Hagenaars (1988)Latent Structure Models with Direct Effects between Indicators
Sociological Methods & Research, 16
K. Jajuga, A. Sokołowski, H. Bock (2002)Classification, Clustering, and Data Analysis: Recent Advances and Applications
A. Dempster, N. Laird, D. Rubin (1977)Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
B. Muthén (2006)Latent VariabLe Hybrids Overview of Old and new Models
L. Goodman (1974)Exploratory latent structure analysis using both identifiable and unidentifiable models
G. Schwarz (1978)Estimating the Dimension of a Model
Annals of Statistics, 6
G. Celeux, G. Govaert (1991)Clustering criteria for discrete data and latent class models
Journal of Classification, 8
A. Formann (1992)Linear Logistic Latent Class Analysis for Polytomous Data
Journal of the American Statistical Association, 87
Cathy Maugis, G. Celeux, M. Martin-Magniette (2009)Variable selection in model-based clustering: A general variable role modeling
Comput. Stat. Data Anal., 53
D. Harper (1972)Local dependence latent structure models
G. Govaert, M. Nadif (2003)Clustering with block mixture models
Pattern Recognit., 36
Isabella Gollini, T. Murphy (2013)Mixture of latent trait analyzers for model-based clustering of categorical data
Statistics and Computing, 24
P. Hattum, H. Hoijtink (2009)Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models
Journal of Classification, 26
J. Vermunt (2003)7. Multilevel Latent Class Models
Sociological Methodology, 33
Émilie Lebarbier, Tristan Mary-Huard (2006)Une introduction au critère BIC : fondements théoriques et interprétation
R. Lebret, S. Iovleff, F. Langrognet, C. Biernacki, G. Celeux, G. Govaert (2015)Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library
Journal of Statistical Software, 67
G. Celeux, G. Govaert (1995)Gaussian parsimonious clustering models
Pattern Recognit., 28
C. Robert (2006)Le Choix Bayésien: principes et pratique
(1996)Mixture Model Clustering of Data Sets with Categorical and Continuous Variables
Y. Qu, M. Tan, M. Kutner (1996)Random effects models in latent class analysis for evaluating accuracy of diagnostic tests.
Biometrics, 52 3
C. Chow, Chao-Ming Liu (1968)Approximating discrete probability distributions with dependence trees
IEEE Trans. Inf. Theory, 14
C. Guinot, J. Latreille, D. Malvy, P. Preziosi, P. Galan, S. Hercberg, M. Tenenhaus (2004)Use of multiple correspondence analysis and cluster analysis to study dietary behaviour: Food consumption questionnaire in the SU.VI.MAX. cohort
European Journal of Epidemiology, 17
(2002)Categorical Data Analysis (Vol. 359)
S. Strauss, D. Rindskopf, Janetta Astone-Twerell, D. Jarlais, H. Hagan (2006)Using latent class analysis to identify patterns of hepatitis C service provision in drug-free treatment programs in the U.S.
Drug and alcohol dependence, 83 1
J. Huang, M. Ng, H. Rong, Zichen Li (2005)Automated variable weighting in k-means type clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 27
(2010)Data Analysis (Vol. 136), Wiley Online Library
B. Reboussin, E. Ip, M. Wolfson (2008)Locally dependent latent class models with covariates: an application to under‐age drinking in the USA
Journal of the Royal Statistical Society: Series A (Statistics in Society), 171
N. Friedman, D. Geiger, M. Goldszmidt (1997)Bayesian Network Classifiers
Machine Learning, 29
E. Allman, C. Matias, J. Rhodes (2008)Identifiability of parameters in latent structure models with many observed variables
Annals of Statistics, 37
(1986)Loglinear Models and Entropy Clustering Methods for Qualitative Data ”
B. Reboussin, Eunyoung Song, A. Shrestha, K. Lohman, M. Wolfson (2006)A latent class analysis of underage problem drinking: evidence from a community sample of 16-20 year olds.
Drug and alcohol dependence, 83 3
M. Espeland, S. Handelman (1989)Using latent class models to characterize and assess relative error in discrete measurements.
Biometrics, 45 2
M. Marbac, C. Biernacki, V. Vandewalle (2014)Model-Based Clustering for Conditionally Correlated Categorical Data
Journal of Classification, 32
L. Hunt, Murray Jorgensen (1999)Theory & Methods: Mixture model clustering using the MULTIMIX program
Australian & New Zealand Journal of Statistics, 41
J. Vermunt (2007)Multilevel mixture item response theory models : An application in education testing
Jie Cheng, R. Greiner (1999)Comparing Bayesian Network Classifiers
M. Chavent, V. Kuentz, J. Saracco (2010)A Partitioning Method for the Clustering of Categorical Variables
J. Banfield, A. Raftery (1993)Model-based Gaussian and non-Gaussian clustering
An extension of the latent class model is presented for clustering categorical data by relaxing the classical “class conditional independence assumption” of variables. This model consists in grouping the variables into inter-independent and intra-dependent blocks, in order to consider the main intra-class correlations. The dependency between variables grouped inside the same block of a class is taken into account by mixing two extreme distributions, which are respectively the independence and the maximum dependency. When the variables are dependent given the class, this approach is expected to reduce the biases of the latent class model. Indeed, it produces a meaningful dependency model with only a few additional parameters. The parameters are estimated, by maximum likelihood, by means of an EM algorithm. Moreover, a Gibbs sampler is used for model selection in order to overcome the computational intractability of the combinatorial problems involved by the block structure search. Two applications on medical and biological data sets show the relevance of this new model. The results strengthen the view that this model is meaningful and that it reduces the biases induced by the conditional independence assumption of the latent class model.
Journal of Classification – Springer Journals
Published: Jul 9, 2015
Access the full text.
Sign up today, get DeepDyve free for 14 days.