Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data [PDF]
, 2015 We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model.
Cederbaum, Jona +3 more
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Function-on-function linear quantile regression
Mathematical Modelling and Analysis, 2022 In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
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Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study. [PDF]
PLoS ONE, 2008 BACKGROUND: Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas.
Susanne Hartmann +6 more
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Axioms, 2022 This paper shows a visual analysis and the dependence relationships of COVID-19 mortality data in 50 states plus Washington, D.C., from January 2020 to 1 September 2022.
Jong-Min Kim
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Contrastive Functional Principal Component Analysis
Proceedings of the AAAI Conference on Artificial IntelligenceAs functional data assumes a central role in contemporary data analysis, the search for meaningful dimension reduction becomes critical due to its inherent infinite-dimensional structure. Traditional methods, such as Functional Principal Component Analysis (FPCA), adeptly explore the overarching structures within the functional data.
Eric Zhang, Didong Li
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Multiple Change-Point Detection in a Functional Sample via the 𝒢-Sum Process
Mathematics, 2022 We first define the G-CUSUM process and investigate its theoretical aspects including asymptotic behavior. By choosing different sets G, we propose some tests for multiple change-point detections in a functional sample.
Tadas Danielius, Alfredas Račkauskas
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New Modeling Approaches Based on Varimax Rotation of Functional Principal Components
Mathematics, 2020 Functional Principal Component Analysis (FPCA) is an important dimension reduction technique to interpret the main modes of functional data variation in terms of a small set of uncorrelated variables.
Christian Acal +2 more
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Properties of principal component methods for functional and longitudinal data analysis
, 2006 The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of ``functional data analysis,'' it has often been assumed that a sample of random functions is observed precisely, in the ...
Hall, Peter +2 more
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Analysing musical performance through functional data analysis: rhythmic structure in Schumann's Träumerei [PDF]
, 2009 Functional data analysis (FDA) is a relatively new branch of statistics devoted to describing and modelling data that are complete functions. Many relevant aspects of musical performance and perception can be understood and quantified as dynamic ...
Almansa, J +1 more
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Functional Principal Component Analysis for Non-stationary Dynamic Time Series [PDF]
, 2018 Motivated by a highly dynamic hydrological high-frequency time series, we propose time-varying Functional Principal Component Analysis (FPCA) as a novel approach for the analysis of non-stationary Functional Time Series (FTS) in the frequency domain ...
Elayouty, Amira +3 more
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