Results 261 to 270 of about 3,232,072 (306)

Functional Data Analysis for Sparse Functional Data

2018
With the development of science and modern technology, more and more data are being collected continuously over a time interval in various disciplines, such as public health, biology, medicine and finance. Such data can be viewed as ``functional data". Functional data analysis (FDA), which deals with the analysis and theory of functional data, has been
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Functional MRI Data Analysis

2018
Functional MRI (fMRI) data analysis aims to characterize neuronal dynamics by using observations of the hemodynamic phenomena associated with neuronal activity. These observations are indirect and highly “noisy” and commonly require different models for data interpretation.
Francisco Gómez, Gabriel Castellanos
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FUNCTIONAL ANALYSIS FOR PARAMETRIC FAMILIES OF FUNCTIONAL DATA

International Journal of Bifurcation and Chaos, 2012
Assuming a Parametric Family of Functional Data, the problem of computing summary statistics of the same functional form is investigated. The central idea is to compile the statistics on the parameters instead of on the functions themselves. With the hypothesis of a monotonic dependence from parameters, we highlight the special features of this ...
DE SANCTIS, Angela Anna, DI BATTISTA, Tonio   +1 more
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Geometric Functional Data Analysis

2019
EPSRC Centre for Doctoral Training in Analysis (Cambridge Centre for Analysis) EP/L016516 ...
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Spatial Functional Data Analysis

2011
We describe a spatial spline regression model, that efficiently deals with data distributed over irregularly shaped regions featuring complex boundaries. The model also accounts for covariate information. Efficient spline bivariate smoothing is achieved by resorting to the finite element method.
J. O. Ramsay, T. Ramsay, SANGALLI, LAURA MARIA   +2 more
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Function Estimation and Functional Data Analysis

1994
The roughness penalty method is widely used in function estimation, and is closely related to methods of regularization well known in numerical analysis. Some background to the development of this method is discussed. The versatility of the method is illustrated by its application to an unusual smoothing problem, involving the estimation of a branching
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Functional Data Analysis

Journal of Educational and Behavioral Statistics, 1999
Eiji Muraki, J. O. Ramsay, B. W. Silverman   +2 more
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Functional data analysis

Choice Reviews Online, 1997
Ramsay, JO, Silverman, BW
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An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy, Donna R Rivera, Jennifer L Lund, Msph   +2 more
exaly  

Discrete functional data analysis

2007
In this paper, we try to extend the framework of Functional Data Analysis (FDA). FDA is an exciting theme that continues development in data analysis. We can sometimes find out valuable information through FDA. Most methods on FDA assume that the functions that represent data are differentiable.
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