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 119 for “"Multivariate data"”.
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The Graphical Representation of Structured Multivariate Data
… summarisation and communication of statistical data. Many graphical techniques exist for exploratory data analysis (ie. for deciding which model it is appropriate to fit to the data) and a number of graphical diagnostic techniques exist for checking the appropriateness of a fitted model. …
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Analysis of Multivariate Data Using Kotz Type Distribution
… of the inferential statistical methods for multivariate data are developed under the fundamental assumption that the data are from a multivariate normal distribution. Unfortunately, one can never be sure a set of data is really from a multivariate normal distribution. There are numerous …
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A framework for the visualization of multidimensional and multivariate data
High dimensionality is a major challenge for data visualization. Parameter optimization problems require an understanding of the behaviour of an objective function in an n-dimensional space around the optimum - this is multidimensional visualization and is a natural extension of the traditional …
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Design and Interpretability of Contour Lines for Visualizing Multivariate Data
Multivariate geospatial data are commonly visualized using contour plots, where the plots for various attributes are often examined side by side, or using color blending. As the number of attributes grows, however, these approaches become less efficient. This limitation motivated the use of glyphs, …
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Inference for High-Dimensional Doubly Multivariate Data under General Conditions
… and theoretical advancements, the amount of data being generated for analysis is growing rapidly. In many cases, the number of subjects may be small, but the number of measurements taken on each subject may be very large. Consider, for example, two groups of patients. The subjects in one …
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Inference for High-Dimensional Doubly Multivariate Data under General Conditions
… and theoretical advancements, the amount of data being generated for analysis is growing rapidly. In many cases, the number of subjects may be small, but the number of measurements taken on each subject may be very large. Consider, for example, two groups of patients. The subjects in one …
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Blind separation of noisy multivariate data using second-order statistics
… k and noise covariance GG-H are unknown. Only a data set X of dimension n > k and of sample size m is observed, where X = AP + GW. The quality of separation depends on source-observation ratio k/n, the degree of spectral diversity, and the second-order non-stationarity of the underlying sources. …
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Visual exploration of multivariate data in breast cancer by dimensional reduction
… of computational techniques based on dimensional data reduction for the visual exploration of DCE-MRI and DNA microarray data in breast cancer. Algorithms for dimensional data reduction aim to compute low-dimensional projections of high-dimensional data while best preserving the data topology. In …
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Metabolic profiling of cyanobacterial extracts using multivariate data analysis for their anti-obesity properties
… Aims. The aim of this study was to perform multivariate data analysis (MVDA) on 117 cyanobacterial extracts to identify potential biomarkers with anti-obesity properties. Methods. Obtained data from UPLC-QTOF-MS was preprocessed by MarkerLynx software to optimize the data evaluation. MVDA …
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Chemometrics applied to food industry: improving quality, safety and sustainability using multivariate data analysis
L'abstract è presente nell'allegato / the abstract is in the attachment
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Multivariate data analysis in Multicolour Flow Cytometry for a deep profiling of the immune system
Contains fulltext : 207491.pdf (Publisher’s version ) (Open Access)
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Maximum likelihood estimation of a multivariate log-concave density
… or difficult to calibrate, especially for multivariate data (nonparametric smoothing methods). We propose an alternative approach using maximum likelihood under a qualitative assumption on the shape of the density, specifically log-concavity. The class of log-concave densities includes many …
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Canonical Correlation Analysis for Longitudinal Data
<p>Data (multivariate data) on two sets of vectors commonly occur in applications. Statistical analysis of these data is usually done using a canonical correlation analysis (CCA). Occurrence of these data at multiple occasions or conditions leads to longitudinal multivariate data for a CCA. We …
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A study of cluster analysis techniques and their applications
… and efficient clustering technique for multivariate data analysis. The technique starts from a multivariate data matrix and ends with graphical representation of the data and pattern recognition discriminant function. The technique also results in distances frequency distribution that …
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NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs
… methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due …
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Extending the Information Partition Function: Modeling Interaction Effects in Highly Multivariate, Discrete Data
Because of the huge amounts of data made available by the technology boom in the late twentieth century, new methods are required to turn data into usable information. Much of this data is categorical in nature, which makes estimation difficult in highly multivariate settings. In this thesis we …
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