Clustering, Cluster Inference and Applications in Clustering: Applications to the Analysis of Gene Expression Data - Softcover
Synopsis
Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generation process. But the problem of choosing the number of components in a statistically meaningful way is still a subject of considerable research. Available methods for estimation include, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting a finite mixture model, based on estimation and inference using a quadratic distance measure. In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods.
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About the Author
Surajit Ray is an assistant professor of Statistics in the Department of Mathematics and Statistics at Boston University. His research interests are in the area of statistical model selection, the theory and geometry of mixture models and functional data analysis. He is especially interested in challenges presented by "large magnitude".
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Clustering, Cluster Inference and Applications in Clustering
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generation process. But the problem of choosing the number of components in a statistically meaningful way is still a subject of considerable research. Available methods for estimation include, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting a finite mixture model, based on estimation and inference using a quadratic distance measure. In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods. 184 pp. Englisch. Seller Inventory # 9783845423623
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Clustering, Cluster Inference and Applications in Clustering
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ray SurajitSurajit Ray is an assistant professor of Statistics in the Department of Mathematics and Statistics at Boston University. His research interests are in the area of statistical model selection, the theory and geometry of mi. Seller Inventory # 5481855
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Clustering, Cluster Inference and Applications in Clustering
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generationprocess. But the problem of choosing the number of components in a statistically meaningful way isstill a subject of considerable research. Available methods for estimationinclude, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting afinite mixture model, based on estimation and inference using a quadratic distance measure.In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch. Seller Inventory # 9783845423623
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Clustering, Cluster Inference and Applications in Clustering | Applications to the Analysis of Gene Expression Data
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Taschenbuch. Condition: Neu. Clustering, Cluster Inference and Applications in Clustering | Applications to the Analysis of Gene Expression Data | Surajit Ray | Taschenbuch | 184 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845423623 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106871119
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Clustering, Cluster Inference and Applications in Clustering : Applications to the Analysis of Gene Expression Data
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multivariate mixture models provide a convenient method of density estimation and model based clustering as well as providing possible explanations for the actual data generation process. But the problem of choosing the number of components in a statistically meaningful way is still a subject of considerable research. Available methods for estimation include, optimizing AIC and BIC, estimating the number through nonparametric maximum likelihood, hypothesis testing and Bayesian approaches with entropy distances. In our book we present several rules for selecting a finite mixture model, based on estimation and inference using a quadratic distance measure. In this book we also develop tools for determining the number of modes in a mixture of multivariate normal densities. We use these criterion to select clusters which display distinct modes. Finally we fine tune our methods to analyze gene-expression data from micro-arrays, and compare them with other competitive methods. Seller Inventory # 9783845423623
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Clustering, Cluster Inference and Applications in Clustering: Applications to the Analysis of Gene Expression Data
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