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.
Results
Showing 1 to 20 of 124 for “"Functional Data"”.
-
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) …
-
Geometric Functional Data Analysis
… 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 smooth manifolds. This is a situation often …
-
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 …
-
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. …
-
Penalized discriminant analysis for multivariate functional data
… discriminant analysis method for multivariate functional data supported on compact 1D domains, motivated by an application that aims to identify subjects with poor cognitive status from diffusion MRI data. By leveraging a connection to the optimal scoring problem, we bypass minimizing complex …
-
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 …
-
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 …
-
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 …
-
Simultaneous White Noise Models and Optimal Recovery of Functional Data
… satisfying prescribed regularity conditions. The data consist of discrete samplings of these realizations in i.i.d. Gaussian noise and the goal is estimation of the underlying trajectories. Further, we want our estimates to enjoy expected L2 errors, conditioned on the realized trajectories, which …
-
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 …
-
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 …
-
Non-Parametric Priors for Functional Data and Partition Labelling Models
… differentiating by how the construction of functional mixture weights</p><p>are handled: one type of process views it as the product of a sequence of marginal</p><p>mixture weights, whereas the other specifies a joint mixture weight for an entire ob-</p><p>servation. These are termed …
-
Flexible models and methods for longitudinal and multilevel functional data
… penalized spline (P-spline)-based methods for functional mixed effects models with varying coefficients. This work is motivated by a clinical study of Complicated Grief (Shear et al. 2005). In the Complicated Grief Study, patients receive active treatment during a treatment period and then …
-
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 …
-
Investigating functional data with sharp local features with applications to spectroscopy
… between the spectra. Indeed, a given peak in a dataset of spectra might be affected by vertical amplifications, horizontal shifts or both simultaneously, the source of those variations possibly being a significant chemical difference or resulting from noise. However, commonly used dissimilarity …
-
Effect size estimation and robust classification for irregularly sampled functional data
Functional data arise frequently in numerous scientific fields with the development of modern technology. Accordingly, functional data analysis to extract information on curves or functions is an important area for investigation. In this thesis, we address two key issues: measuring an effect size …
-
A functional data analytic approach for region level differential DNA methylation detection
… This study focuses on developing two techniques, functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real …
Page 1 of 7