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 6602 for “"Data analysis"”.
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Spatial Data Analysis
… with a focus on modeling spatial and temporal data. In chapter 1, we explained different terminology and principles that appear frequently in the analysis of spatial and temporal data. These concepts were explained in detail to form a basis and motivation for the research work. In particular, …
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Adaptive Functional Data Analysis
… we contribute to adaptive modeling of functional data, focusing on the fundamental aspects of representation and regression, where challenges arise from the infinite-dimensionality of their underlying spaces. For adaptive representation, the notion of mixture inner product spaces (MIPS) is …
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Geometric Functional Data Analysis
… we introduce a comprehensive framework for the analysis of statistical samples that are functional data with non-trivial geometry. Geometry can interplay with functional data in different forms. The most general setting considered here is that of functional data supported on random non-linear …
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Contributions to Functional Data Analysis
Functional data consist of repeated measurements taken over time for each subject. The data for a subject are assumed to be values of a random function that is observed at a discrete time points rather than a sequence of individual measurements. Functional data are classified dense or sparse based …
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Uncertainties in climate data analysis
… what we do not know and by how much. In climate data analysis, this involves an accurate specification of measured quantities and a consequent analysis that consciously propagates the measurement errors at each step. The dissertation presents a thorough analytical method to quantify errors of …
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Some techniques of data analysis
Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979.
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Efficient Privacy-Aware Imagery Data Analysis
… devices triggers an explosive growth of imagery data. To extract and process the rich contents contained in imagery data, various image analysis techniques have been investigated and applied to a spectrum of application scenarios. In recent years, breakthroughs in deep learning have powered a new …
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Kernel Estimators in Complex Data Analysis
… easy interpretation and flexibility to model data with complicated density curves/conditional mean curves. Even during this information age, when the datasets confronting us have become larger and more complicated, which seems to disfavor the use of kernel estimators because of the so- called …
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Data analysis of continuous gravitational waves
… our software, we have worked on simulated data as well as hardware injected signals of pulsars in the fourth LIGO science run (S4). While with the current sensitivity of our detectors we do not expect to detect any true Gravitational Wave signals in our data, we can still set upper limits …
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Essays on Large Panel Data Analysis
… finance have attempted to utilize large panel data sets. Large panel data sets contain rich information on the dynamics of many cross-sectional units over long time periods. These data sets often consist of numerous series in different categories that reflect the multifaceted aspects of an …
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Statistical methods for fMRI data analysis
… medical-imaging modality, which means the data structure is complicated and the data size is huge. These features of fMRI data pose some challenges to traditional statistical methods which focus on data with smal sample size and simple data structure. The functional activation detection and …
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Deep learning in sequential data analysis
… into deep learning based methods for sequential data such as videos and medical image sequences. With the extra information from its additional sequential dimension, sequential data naturally raises an important and challenging question: How can we effectively and efficiently integrate such …
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Probabilistic data analysis with probabilistic programming
Probabilistic techniques are central to data analysis, but dierent approaches can be challenging to apply, combine, and compare. This thesis introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and …
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A tool for hemodynamic data analysis
… is capable of processing and navigating large data sets of blood pressure and cerebral blood flow. Large data sets are important because the events that cause brain injury are believed to be short-lived, possibly infrequent, and unpredictable. Additionally, since this is a relatively unexplored …
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Microarray data analysis methods and their applications to gene expression data analysis for Saccharomyces cerevisiae under oxidative stress
… at 3 minute after the exposure. Statistical analysis methods, including ANOVA, k-means clustering analysis, and pathway analysis were used to analyze the data. The results from this study provide a dynamic resolution of the oxidative stress responses in S. cerevisiae, and contribute to a …
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