Results 61 to 70 of about 1,029,479 (280)
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
core +3 more sources
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
core +2 more sources
Functional quantile principal component analysis
BiostatisticsSummary This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves.
Méndez-Civieta, Álvaro +3 more
openaire +2 more sources
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
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
core
Sparse Principal Component Analysis via Fractional Function Regularity [PDF]
Mathematical Problems in Engineering, 2020 In this paper, we describe a novel approach to sparse principal component analysis (SPCA) via a nonconvex sparsity-inducing fraction penalty function SPCA (FP-SPCA). Firstly, SPCA is reformulated as a fraction penalty regression problem model. Secondly, an algorithm corresponding to the model is proposed and the convergence of the algorithm is ...
Xuanli Han +3 more
openaire +2 more sources
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source
Time Alignment as a Necessary Step in the Analysis of Sleep Probabilistic Curves
Measurement Science Review, 2018 Sleep can be characterised as a dynamic process that has a finite set of sleep stages during the night. The standard Rechtschaffen and Kales sleep model produces discrete representation of sleep and does not take into account its dynamic structure.
Rošt’áková Zuzana, Rosipal Roman
doaj +1 more source
, 2018 Functional data typically contains amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, although it may contain valuable information.
Gabriel, Alice-Agnes +3 more
core +1 more source
Sparse and Functional Principal Components Analysis [PDF]
2019 IEEE Data Science Workshop (DSW), 2019 The published version of this paper incorrectly thanks "Luofeng Luo" instead of "Luofeng Liao" in the ...
Allen, Genevera I., Weylandt, Michael
openaire +2 more sources