Results 1 to 10 of about 1,020,025 (280)
Conditional Functional Principal Components Analysis [PDF]
Scandinavian Journal of Statistics, 2006 Abstract. This work proposes an extension of the functional principal components analysis (FPCA) or Karhunen–Loève expansion, which can take into account non‐parametrically the effects of an additional covariate. Such models can also be interpreted as non‐parametric mixed effect models for functional data.
openaire +2 more sources
Functional principal component analysis of fMRI data [PDF]
Human Brain Mapping, 2004 AbstractWe describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used ...
Roberto, Viviani +2 more
openaire +2 more sources
On Properties of Functional Principal Components Analysis [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2005 SummaryFunctional data analysis is intrinsically infinite dimensional; functional principal component analysis reduces dimension to a finite level, and points to the most significant components of the data. However, although this technique is often discussed, its properties are not as well understood as they might be.
Hall, Peter, Hosseini-Nasab, Seyed
openaire +2 more sources
Adaptive Functional Principal Component Analysis
, 2023 We introduce Adaptive Functional Principal Component Analysis, a novel method to capture directions of variation in functional data that exhibit sharp changes in smoothness. We first propose a new adaptive scatterplot smoothing technique that is fast and scalable, and then integrate this technique into a probabilistic FPCA framework to adaptively ...
de la Garza, Angel Garcia +3 more
openaire +2 more sources
Properties of design-based functional principal components analysis [PDF]
Journal of Statistical Planning and Inference, 2010 Revised version for J. of Statistical Planning and Inference (January 2009)
Cardot, Hervé +3 more
openaire +3 more sources
Eigen-Adjusted Functional Principal Component Analysis
Journal of Computational and Graphical Statistics, 2022 Functional Principal Component Analysis (FPCA) has become a widely-used dimension reduction tool for functional data analysis. When additional covariates are available, existing FPCA models integrate them either in the mean function or in both the mean function and the covariance function.
Ci-Ren Jiang +3 more
openaire +2 more sources
An Advanced Hybrid Logistic Regression Model for Static and Dynamic Mixed Data Classification
IEEE Access, 2022 We consider the binary classification problem of static and dynamic mixed data in this paper. Different from mixed categorical and numerical data, the dynamic variables in the new type of data vary with time and are recorded at discrete time points. This
Mingxue Quan
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source