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2006
In many different fields of applied statistics the object of interest is depending on some continuous parameter, i.e. continuous time. Typical examples in biostatistics are growth curves or temperature measurements. Although for technical reasons, we are able to measure temperature just in discrete intervals — it is clear that temperature is a ...
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In many different fields of applied statistics the object of interest is depending on some continuous parameter, i.e. continuous time. Typical examples in biostatistics are growth curves or temperature measurements. Although for technical reasons, we are able to measure temperature just in discrete intervals — it is clear that temperature is a ...
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Functional Data Analysis for Sparse Functional Data
2018With 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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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 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, 2012Assuming 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 +1 more
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Geometric Functional Data Analysis
2019EPSRC Centre for Doctoral Training in Analysis (Cambridge Centre for Analysis) EP/L016516 ...
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Spatial Functional Data Analysis
2011We 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 +2 more
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Function Estimation and Functional Data Analysis
1994The 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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