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- Bayesian (1)
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- Functional connectivity; spatio-temporal dependency; EM algorithm; functional magnetic resonance imaging (fMRI); generalized fused lasso; group lasso; graphical lasso; network estimation; Gaussian graphical model; high-dimensional (1)
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Articles 1 - 4 of 4
Full-Text Articles in Multivariate Analysis
Statistical Approaches For Estimation And Comparison Of Brain Functional Connectivity, Jifang Zhao
Statistical Approaches For Estimation And Comparison Of Brain Functional Connectivity, Jifang Zhao
Theses and Dissertations
Drug addiction can lead to many health-related problems and social concerns. Functional connectivity obtained from functional magnetic resonance imaging (fMRI) data promotes a variety of fundamental understandings in such association. Due to its complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computationally efficient algorithm to estimate the parameters. Our method is used to identify functional connectivity and …
Zero-Inflated Longitudinal Mixture Model For Stochastic Radiographic Lung Compositional Change Following Radiotherapy Of Lung Cancer, Viviana A. Rodríguez Romero
Zero-Inflated Longitudinal Mixture Model For Stochastic Radiographic Lung Compositional Change Following Radiotherapy Of Lung Cancer, Viviana A. Rodríguez Romero
Theses and Dissertations
Compositional data (CD) is mostly analyzed as relative data, using ratios of components, and log-ratio transformations to be able to use known multivariable statistical methods. Therefore, CD where some components equal zero represent a problem. Furthermore, when the data is measured longitudinally, observations are spatially related and appear to come from a mixture population, the analysis becomes highly complex. For this matter, a two-part model was proposed to deal with structural zeros in longitudinal CD using a mixed-effects model. Furthermore, the model has been extended to the case where the non-zero components of the vector might a two component mixture …
Dimension Reduction And Variable Selection, Hossein Moradi Rekabdarkolaee
Dimension Reduction And Variable Selection, Hossein Moradi Rekabdarkolaee
Theses and Dissertations
High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. …
Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers
Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers
Theses and Dissertations
The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used …