Results 51 to 60 of about 1,020,025 (280)
Principal Component Analysis for Functional Data [PDF]
, 2001 In functional principal component analysis (PCA), we treat the data that consist of functions not of vectors (Ramsay and Silverman, 1997). It is an attractive methodology, because we often meet the cases where we wish to apply PCA to such data.
Tanaka, Yutaka, Yamanishi, Yoshihiro
core +1 more source
Forecasting Canadian Age-Specific Mortality Rates: Application of Functional Time Series Analysis
Mathematics, 2023 In the insurance and pension industries, as well as in designing social security systems, forecasted mortality rates are of major interest. The current research provides statistical methods based on functional time series analysis to improve mortality ...
Azizur Rahman, Depeng Jiang
doaj +1 more source
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
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR HIGH-DIMENSIONAL DATA [PDF]
, 2010 We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits. The proposed inferential methods avoid the difficult task of loading the entire data set at once in the ...
Caffo, Brian +5 more
core +2 more sources
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
doaj +1 more source
Sparse multivariate functional principal component analysis
Stat, 2022 We introduce a sparse multivariate functional principal component analysis method by incorporating ideas from the group sparse maximum variance method to multivariate functional data. Our method can avoid the “curse of dimensionality” from a high‐dimensional dataset and enjoy interpretability at the same time. In particular, our unsupervised method can
Jun Song, Kyongwon Kim
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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
doaj +1 more source
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
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
Functional principal component analysis for cointegrated functional time series
Journal of Time Series Analysis, 2023 Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool.
openaire +2 more sources