Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 24 for “"Model-based clustering"”.
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Advances in mixture modeling and model based clustering
… in heterogeneous data. There are several clustering approaches with the goal of minimizing the within cluster variance while maximizing the variance between clusters. K-means or hierarchical clustering with different linkages can be thought as distance-based approaches. Another approach is …
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Bayesian model-based clustering of multi-source data
… across multiple sources. Bayesian mixture models and their extensions are effective tools for partition inference in this setting as we can use these to describe and infer the relationship between different sources. I consider applying such methods to two cases of multi-source data: …
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Model-based clustering for multivariate time series of counts
This dissertation develops a modeling framework for univariate and multivariate zero-inflated time series of counts and applies the models in a clustering scheme to identify groups of count series with similar behavior. The basic modeling framework used is observation-driven Poisson regression with …
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Variable Selection for DNA Methylation Data Using Model-Based Clustering
… cohort study. The approach featured the use of clustering with a penalty function to select informative variables. We evaluated the method by conducting simulations for a variety of scenarios. For clustering, we evaluated sensitivity and specificity, and for variable selection, we evaluated the …
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Simultaneous clustering with mixtures of factor analysers
This work details the method of Simultaneous Model-based Clustering. It also presents an extension to this method by reformulating it as a model with a mixture of factor analysers. This allows for the technique, known as Simultaneous Model-Based Clustering with a Mixture of Factor Analysers, to be …
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Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
… it is convenient, the conventional single model approach may fail to accurately describe the relationships between variables. Two alternative modeling approaches are available: one applies separate models for different regions; the other applies hierarchical models. The separate modeling …
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Clustering Analysis of Zernike Coefficients Through Quantile Regression
<p>In this thesis, we use the model-based clustering procedure to cluster fifteen Zernike coefficients into groups. Quantile regressions are considered to describe the relationship between Zernike coefficients and pupil size. We employ Gibbs sampler and adaptive rejection Metropolis sampling to …
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Some Model-Based and Distance-Based Clustering Methods for Characterization of Regional Ecological Stressor-Response Patterns and Regional Environmental Quality Trends
… variation in regression relationships, we use model-based clustering procedures with class-specific regression models. Units for clustering are taken to be basins, or combinations of basins and ecoregions. We rely on a Bayesian formulation and sample the posterior distribution using a Markov …
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Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm
… is not natural or evident, such as mixed-effects models, mixture models, log-linear models, and latent variables. In Chapter 2 of this thesis, we apply the EM algorithm to grouped data, a problem in which incomplete data are evident. Nowadays, data confidentiality is of great importance for many …
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Statistical methods for fMRI data analysis
… estimation; in the second part, we propose a model based clustering method to detect the functional connectivity network; in the third part, we present a general and novel statistical framework for robust and more complete estimation of brain functional connectivity based on correlation …
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Model-based Cluster Analysis Using Variables Characterizing Types of Democracy
<p> This thesis applies model-based cluster analysis to data concerning types of democracies, creating an instrument for typologies which attempt conceptual classification based on an explicit theory. We note several advantages of model-based clustering over traditional clustering methods, …
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A self-learning framework for validation of runtime adaptation in service-oriented systems
… adaptation in service-oriented systems, through model-based clustering and deep learning. To evaluate the efficacy of the approach a medium sized health care case study was devised and implemented. The results obtained show that self-validation significantly improves the dynamic adaptation …
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Quantitative analysis of cerebral white matter anatomy from diffusion MRI
… to the prototype center of each bundle. Based on the computed distances we also develop a novel model-based clustering of trajectories into anatomically-known fiber bundles. In order to cluster the trajectories, we formulate an expectation maximization algorithm to infer the parameters of …
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Variant Detection Using Next Generation Sequencing Data
… which cannot be detected using microarray based technologies. However, enormous amounts of raw sequence data generated from NGS technologies pose great challenges for data analysis. Efficient computational algorithms and tools to analyze these data are in great need. In this dissertation, …
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PROBABILISTIC AND DEEP LEARNING APPROACHES TO MODELING BIOLOGICAL SYSTEMS
… probabilistic and deep learning methods for modeling biological systems across various scales, with a specific focus on cancer. The aim is to develop models that are both quantitatively rigorous and biologically meaningful. In the first part, I present a hierarchical Bayesian extension of …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
… in data. In traditional nonparametric mixture models, observations are usually assumed exchangeable, even though dependence often exists associated with the space or time at which data are generated.</p> <p>Focused on model-based clustering and segmentation, this thesis addresses the issue in …
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Statistical Methods for Multivariate Functional Data Clustering, Recurrent Event Prediction, and Accelerated Degradation Data Analysis
… Specifically, in Chapter 2, we introduce a clustering method for multivariate functional data. In order to cluster the customized events extracted from multivariate functional data, we apply the functional principal component analysis (FPCA), and use a model based clustering method on a …
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Variational Mixture Models for non-Gaussian observations: Applications to molecular data
… diseases to specific methylation patterns. Clustering analysis and posterior feature selection of the most important genetic loci that discriminate each subgroup of individuals are the two tools we suggest for achieving this venture. Clustering DNA methylation data though is not a trivial …
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Clustering Response-Stressor Relationships in Ecological Studies
… by human activities. The conventional single model approach based on regression and logistic regression usually fails to adequately model the relationship between biological responses and environmental stressors since the study samples are collected over a large spatial region and the …
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Statistical Modelling of Mosquito Abundance and West Nile Virus Risk with Weather Conditions
… data and built the statistical forecasting models to predict the West Nile virus risk. In the first part, using mosquito data from the surveillance program in Peel Region, Ontario, we studied the distribution properties of Culex mosquito abundance data for the period from 2004 to 2012. We …
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