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The SIMCLAS Model: Simultaneous Analysis of Coupled binary data Matrices with Noise Heterogeneity Between and Within data Blocks

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PSYCHOMETRIKA 2012年第4期77卷 724-740页

作者： Wilderjans, Tom F. Ceulemans, E. Van Mechelen, I. Katholieke Univ Leuven Dept Psychol Res Grp Quantitat Psychol & Individual Difference B-3000 Louvain Belgium Katholieke Univ Leuven Dept Educ Sci B-3000 Louvain Belgium

In many research domains different pieces of information are collected regarding the same set of objects. Each piece of information constitutes a data block, and all these (coupled) blocks have the object mode in common. When analyzing such data, an important aim is to obtain an overall picture of the structure underlying the whole set of coupled data blocks. A further challenge consists of accounting for the differences in information value that exist between and within (i.e., between the objects of a single block) data blocks. To tackle these issues, analysis techniques may be useful in which all available pieces of information are integrated and in which at the same time noise heterogeneity is taken into account. For the case of binary coupled data, however, only methods exist that go for a simultaneous analysis of all data blocks but that do not account for noise heterogeneity. Therefore, in this paper, the SIMCLAS model, being a Hierarchical Classes model for the simultaneous analysis of coupled binary two-way matrices, is presented. In this model, noise heterogeneity between and within the data blocks is accounted for by downweighting entries from noisy blocks/objects within a block. In a simulation study it is shown that (1) the SIMCLAS technique recovers the underlying structure of coupled data to a very large extent, and (2) the SIMCLAS technique outperforms a Hierarchical Classes technique in which all entries contribute equally to the analysis (i.e., noise homogeneity within and between blocks). The latter is also demonstrated in an application of both techniques to empirical data on categorization of semantic concepts.

关键词： data fusion coupled data multi-set data noise heterogeneity simultaneous clusterings Hierarchical Classes Analysis overlapping clustering hierarchical relations multivariate binary data

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Conditional independence of multivariate binary data with an application in caries research

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COMPUTATIONAL STATISTICS & data ANALYSIS 2007年第6期51卷 3223-3234页

作者： Garcia-Zattera, Maria Jose Jara, Alejandro Lesaffre, Emmanuel Declerck, Dominique Catholic Univ Louvain Ctr Biostat B-3000 Louvain Belgium Catholic Univ Louvain Sch Dent B-3000 Louvain Belgium

For the analysis of caries experience in seven-year old children the association between the presence or absence of caries experience among deciduous molars within each child is explored. Some of the high associations have an etiological basis (e.g., between symmetrically opponent molars), while others (diagonally opponent molars) are assumed to be the result of the transitivity of association and to disappear once conditioned on the caries experience status of the other deciduous molars, covariates and random effects. However, using discrete models for multivariate binary data, conditioning does not remove the diagonal association. When the association is explored on a latent scale, e.g., by a multivariate probit model, then conditional independence can be concluded. This contrast is confirmed when using other models on the (observed) binary scale and on the latent scale. Depending on the point of view, the differences in conditional independence might be seen as a consequence of different types of measurements or as a consequence of different models. An example shows that the results and conclusions can be markedly different with important consequences on model building. The explanation for this result is exemplified mathematically and illustrated using dental data from the Signal-Tandmobiel((R)) study. (c) 2006 Elsevier B.V. All rights reserved.

关键词： conditional independence multivariate binary data latent variable representation multivariate probit model

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Factors affecting power of tests for multiple binary outcomes

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STATISTICS IN MEDICINE 2010年第28期29卷 2890-2904页

作者： Mascha, Edward J. Imrey, Peter B. Cleveland Clin Dept Quantitat Hlth Sci & Outcomes Res Cleveland OH 44195 USA Cleveland Clin Dept Quantitat Hlth Sci Cleveland OH 44195 USA Cleveland Clin Mellen Ctr Multiple Sclerosis Treatment & Res Cleveland OH 44195 USA

Frequently in clinical studies a primary outcome is formulated from a vector of binary events Several methods exist to assess treatment effects on multiple correlated binary outcomes including comparing groups on the occurrence of at least one among the outcomes (collapsed composite), on the count of outcomes observed per subject, on individual outcomes adjusting for multiplicity, or with multivariate tests postulating either common or distinct effects across outcomes We focus on a 1 df distinct effects test in which the estimated outcome specific treatment effects from a GEE model are simply averaged, and compare it with other methods on clinical and statistical grounds Using a flexible method to simulate multivariate binary data, we show that the relative efficiencies of the assessed tests depend in a complex way on the magnitudes and variabilities of component incidences and treatment effects as well as correlations among component events While other tests are easily 'driven by high frequency components the average effect GEE test is not, since It averages the log odds ratios unweighted by the component frequencies Thus, the average effect test is relatively more powerful than other tests when lower frequency components have stronger associations with a treatment or other predictor, but less powerful when higher frequency components are more strongly associated In studies when relative effects are at least as Important as absolute effects or when lower frequency components are clinically most Important this test may be preferred Two clinical trials are discussed and analyzed and recommendations for practice are made Copyright (C) 2010 John Wiley & Sons, Ltd

关键词： composite outcomes generalized estimating equations multivariate binary data correlated binary outcomes relative efficiency clinical trials

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Selection of binary variables and classification by boosting

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2007年第4期36卷 855-869页

作者： Park, Junyong Wilbur, Jayson D. Ghosh, Jayanta K. Nakatsu, Cindy H. Ackerman, Corinne Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA Worcester Polytech Inst Dept Math Sci Worcester MA 01609 USA Purdue Univ Dept Stat W Lafayette IN 47907 USA Purdue Univ Dept Agron W Lafayette IN 47907 USA

We adopt boosting for classification and selection of high-dimensional binary variables for which classical methods based on normality and non singular sample dispersion are inapplicable. Boosting seems particularly well suited for binary variables. We present three methods of which two combine boosting with the relatively classical variable selection methods developed in Wilbur et al. (2002). Our primary interest is variable selection in classification with small misclassification error being used as validation of proposed method for variable selection. Two of the new methods perform uniformly better than Wilbur et al. (2002) in one set of simulated and three real life examples.

关键词： boosting cross validation DNA fingerprints high-dimensional data multivariate binary data thresholding variable selection

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Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints

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BIOMETRICS 2002年第2期58卷 378-386页

作者： Wilbur, JD Ghosh, JK Nakatsu, CH Brouder, SM Doerge, RW Purdue Univ Dept Stat W Lafayette IN 47907 USA Indian Stat Inst Kolkata 700035 W Bengal India Purdue Univ Dept Agron W Lafayette IN 47907 USA

In order to understand the relevance of microbial communities on crop productivity, the identification and characterization of the rhizosphere soil microbial community is necessary. Characteristic profiles of the microbial communities are obtained by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16S rDNA from soil extracted DNA. These characteristic profiles, commonly called community DNA fingerprints, can be represented in the form of high-dimensional binary vectors. We address the problem of modeling and variable selection in high-dimensional multivariate binary data and present an application of our methodology in the context of a controlled agricultural experiment.

关键词： classification DNA fingerprints high-dimensional data microbial communities multivariate binary data permutation tests variable selection

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Subset clustering of binary sequences, with an application to genomic abnormality data

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BIOMETRICS 2005年第4期61卷 1027-1036页

作者： Hoff, PD Univ Washington Dept Stat Seattle WA 98195 USA Univ Washington Dept Biostat Seattle WA 98195 USA Univ Washington Ctr Stat & Social Sci Seattle WA 98195 USA

This article develops a model-based approach to clustering multivariate binary data, in which the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The clustering approach is based on a multivariate Dirichlet process mixture model, which allows for the estimation of the number of clusters, the cluster memberships, and the cluster-specific parameters in a unified way. Such a clustering approach has applications in the analysis of genomic abnormality data, in which the development of different types of tumors may depend on the presence of certain abnormalities at subsets of locations along the genome. Additionally, such a mixture model provides a nonparametric estimation scheme for dependent sequences of binary data.

关键词： genetic pathway multivariate binary data nonparametric bayes unsupervised learning

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Efficiency of generalized estimating equations for binary responses

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2004年第4期66卷 851-860页

作者： Rao Chaganty, N Joe, H Old Dominion Univ Dept Math & Stat Norfolk VA 23529 USA Univ British Columbia Vancouver BC V5Z 1M9 Canada

Using standard correlation bounds, we show that in generalized estimation equations (GEEs) the so-called 'working correlation matrix'R(alpha) for analysing binary data cannot in general be the true correlation matrix of the data. Methods for estimating the correlation param-eter in current GEE software for binary responses disregard these bounds. To show that the GEE applied on binary data has high efficiency, we use a multivariate binary model so that the covariance matrix from estimating equation theory can be compared with the inverse Fisher information matrix. But R(alpha) should be viewed as the weight matrix, and it should not be confused with the correlation matrix of the binary responses. We also do a comparison with more general weighted estimating equations by using a matrix Cauchy-Schwarz inequality. Our analysis leads to simple rules for the choice of alpha in an exchangeable or autoregressive AR(1) weight matrix R(alpha), based on the strength of dependence between the binary variables. An example is given to illustrate the assessment of dependence and choice of alpha.

关键词： generalized estimating equations multivariate binary data odds ratio quasi-least squares repeated measurements

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On the use of Bernoulli mixture models for text classification

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PATTERN RECOGNITION 2002年第12期35卷 2705-2710页

作者： Juan, A Vidal, E Univ Politecn Valencia Dep Sistemas Informat & Comp Valencia 46022 Spain

Mixture modelling of class-conditional densities is a standard pattern recognition technique. Although most research on mixture models has concentrated on mixtures for continuous data, emerging pattern recognition applications demand extending research efforts to other data types. This paper focuses on the application of mixtures of multivariate Bernoulli distributions to binary data. More concretely, a text classification task aimed at improving language modelling for machine translation is considered. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： mixture models EM algorithm data categorization multivariate binary data text classification multivariate Bernoulli distribution

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Bayesian predictiveness, exchangeability and sufficientness in bacterial taxonomy

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MATHEMATICAL BIOSCIENCES 2002年第Spe1期177卷 161-184页

作者： Gyllenberg, M Koski, T Univ Turku Dept Math Turku 20014 Finland Linkoping Univ Dept Math S-58183 Linkoping Sweden

We present a theory of classification and predictive identification of bacteria. Bacterial strains are characterized by a binary vector and the taxonomy is specified by attaching a label to each vector. The theory is developed from only two basic assumptions, viz. that the sequence of pairs of feature vectors and the attached labels is judged (infinitely) exchangeable and predictively sufficient. We derive expressions for the training error and the probability of identification error and show that latter is an affine function of the former. We prove the law of large numbers for identification matrices, which contain the fundamental information of bacterial data. We prove the Bayesian risk consistency of the predictive identification rule given by the theory and show that the training error is a consistent estimate of the generalization error. (C) 2002 Published by Elsevier Science Inc.

关键词： multivariate binary data Bayesian risk consistency Bahadur-Lazarsfeld expansions supervised learning multivariate Bernoulli distributions

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Bayesian predictiveness, exchangeability and sufficientness in bacterial taxonomy

Bayesian predictiveness, exchangeability and sufficientness ...

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2nd International Conference on Deterministic and Stochastic Modeling of Biological Interaction (DESTEBIO 2000)

作者： Gyllenberg, M Koski, T Univ Turku Dept Math Turku 20014 Finland Linkoping Univ Dept Math S-58183 Linkoping Sweden

关键词： multivariate binary data Bayesian risk consistency Bahadur-Lazarsfeld expansions supervised learning multivariate Bernoulli distributions

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