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 38 for “"Functional data analysis."”.
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Adaptive Functional Data Analysis
… thesis, 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) …
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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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Linear mixed effects models in functional data analysis
Regression models with a scalar response and a functional predictor have been extensively studied. One approach is to approximate the functional predictor using basis function or eigenfunction expansions. In the expansion, the coefficient vector can either be fixed or random. The random coefficient …
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Applications of functional data analysis to environmental problems.
Functional Data Analysis (FDA) is a relatively recent framework within the statistical sciences, and while it offers compelling benefits to many applications, it has not yet gained widespread applied use. Two important environmental applications, water quality profile forecasting and larval fish …
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Exploring and Modeling Online Auctions Using Functional Data Analysis
… arrives with enormous amounts of rich and clean data as well as statistical challenges. eCommerce not only creates new data challenges, it also motivates the need for innovative models. While there exist many theories about economic behavior of participants in market exchanges, many of these …
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Topics in functional data analysis and machine learning predictive inference
… composed of three research projects focused on functional data analysis and machine learning predictive inference.</p> <p>The first project deals with the covariance estimation, principal component analysis, and prediction of spatially correlated functional data. We develop a general framework …
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Comparison of Time Series and Functional Data Analysis for the Study of Seasonality.
<p>Classical time series analysis has well known methods for the study of seasonality. A more recent method of functional data analysis has proposed phase-plane plots for the representation of each year of a time series. However, the study of seasonality within functional data analysis has not been …
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Assessment of control and performance of biomedical systems
… vary between patients). Statistical methodology: Functional data analysis and multilevel modelling are utilised in the investigation of these two biomedical systems. Functional data analysis considers observations as a function rather than a highly correlated sequence of measurements. Multilevel …
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Robust Statistical Modeling In Functional Linear Regression
Functional linear regression is a prominent field within the domain of functional data analysis, with extensive applications in various domains such as biomedical studies, brain imaging, and chemometrics. However, despite the abundance of literature on functional linear regression, limited …
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Functional linear regression on Namibian and South African data
… between geographically spate populations with functional regression analysis using climate variables at each location. A number of statistical challenges present themselves such as the multivariate nature of the data. Functional data analysis was used in this project to display the data so as …
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TESTING THE EQUALITY OF SEVERAL COVARIANCE FUNCTIONS FOR FUNCTIONAL DATA
In functional data analysis, one-way ANOVA problems have been studied by many researchers in recent decades. And the equal-covariance assumption is commonly assumed in these equal-mean function testing problems. So it is of interest to check whether this assumption holds or not. In this thesis, we …
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Functional data methods for climatological processes
… these observations can be considered as functional data and the tools for studying the behavior of functional data define a framework known as Functional Data Analysis (FDA). In the following Chapters we will propose three FDA methods to model three different climatological phenomena. …
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A data-centric stochastic model for simulation of occupant-related energy demand in buildings
… uncertainty, but must also be able to assimilate data, be able to simulate operational change and be straightforward to use. All buildings generate monitored data of some form, even if it is just monitored consumption for purposes of billing. Since the start of the century there has been a rapidly …
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An investigation into Functional Linear Regression Modeling
Functional data analysis, commonly known as FDA", refers to the analysis of information on curves of functions. Key aspects of FDA include the choice of smoothing techniques, data reduction, model evaluation, functional linear modeling and forecasting methods. FDA is applicable in numerous …
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Methods for comparison and analysis of spatiotemporal fields
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Functional Data Models for Raman Spectral Data and Degradation Analysis
Functional data analysis (FDA) studies data in the form of measurements over a domain as whole entities. Our first focus is on the post-hoc analysis with pairwise and contrast comparisons of the popular functional ANOVA model comparing groups of functional data. Existing contrast tests assume …
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Topological and geometric inference of data
… in practice. Following ideas from topological data analysis, simplicial complexes are used as discrete analogues of spaces suitable for computation. By utilising the prior assumption that the data lie on a manifold, topologically inspired techniques are proposed for refining the simplicial …
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High-Dimensional Functional Graphs and Inference for Unknown Heterogeneous Populations
… for analyzing high-dimensional, heterogeneous functional data, focusing specifically on uncovering hidden patterns and network structures within such complex data. We utilize functional graphical models (FGMs) to explore the conditional dependence structure among random elements. We mainly …
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