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 13 of 13 for “"covariance models"”.
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Structural RNA Homology Search and Alignment Using Covariance Models
… examples of homologous RNAs and comparing them. Covariance models: CMs) are powerful computational tools for homology search and alignment that score both the conserved sequence and secondary structure of an RNA family. However, due to the high computational complexity of their search and …
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RNA secondary structure detection programs with an emphasis on covariance models
… better conserved than their primary structure. Covariance models, probabilistic models that utilize stochastic-context-free grammars, are one approach. CMs allow for homology to be detected where purely sequence-based methods would fail. A background on CMs is given, as well as a background of …
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Novel Algorithms for Structural Alignment of Non-coding RNAs
… challenges for sequence analysis. Probabilistic covariance models are effective representations of structural RNAs, with generally high sensitivity and specificity but slow computational speed. New algorithms for dealing with structural RNAs are developed to address some of the practical …
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Predictive parameter estimation for Bayesian filtering
… I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle …
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Topics in Bayesian Spatiotemporal Prediction of Environmental Exposure
… these ideas for model comparison where we fit models of interest to a portion of the data and hold out the rest for model comparison.</p><p>In Chapters 3 and 4, we consider pollution data from Mexico City in 2017. In Chapter 3 we forecast pollution emergencies. Mexico City defines pollution …
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High-throughput experimental and computational studies of bacterial evolution
… consisting of two chapters, uses statistical models of sequence variation, i.e. covariance models, to examine the evolution of intrinsic termination across the bacterial kingdom. A first collaborative study provides background and motivation in the form of a method for identifying …
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Methods and applications for space-time data
… for both separable and nonseparable space-time covariance models. The model is also illustrated with wind speed and streamflow datasets. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance …
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The algebraic statistics of sampling, likelihood, and regression
This thesis is about statistical models and algebraic varieties. Algebraic Statistics unites these two concepts, turning algebraic structure into statistical insight. Featured here are three types of models that have such an algebraic structure. Linear Gaussian covariance models are continuous …
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A non-convex framework for structured non-stationary covariance recovery theory and application
Flexible, yet interpretable, models for the second-order temporal structure are needed in scientific analyses of high-dimensional data. The thesis develops a structured time-indexed covariance model for non-stationary time-series data by decomposing them into sparse spatial and temporally smooth …
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Sex Differences in Alzheimer’s Disease Neuroimaging Biomarkers among Cognitively Normal Older Adults
… levels (SUVR = 7.27). Multivariate analysis of covariance models adjusted for age, education, and modality specific confounders demonstrated lower volume (F (5, 103) = 13.56, p = <0.001) and higher blood flow F (7, 102) = 2.58, p = 0.017) among women compared to men in AD pathology and estrogen …
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Modeling spatial covariance functions
<p>Covariance modeling plays a key role in the spatial data analysis as it provides important information about the dependence structure of underlying processes and determines performance of spatial prediction. Various parametric models have been developed to accommodate the idiosyncratic features …
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An Examination of a Mindfulness-Based Intervention for Older Adults
… (group) by four (time of assessment) analysis of covariance models were estimated to evaluate primary outcomes. Results indicated that there was no significant treatment effect on primary outcomes. However, the mindfulness-based intervention was feasible and acceptable. Gaining additional …
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Optimal spectral reconstructions from deterministic and stochastic sampling geometries using compressive sensing and spectral statistical models
… approach based on the use of isotropic models over a dyadic partitioning of the spectrum. The proposed methods are demonstrated in applications in reconstructing fMRI and remote sensing imagery. Typically, a reduction in MRI image acquisition time is achieved by sampling K-space at a …