Results 41 to 50 of about 17,164 (153)
FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING [PDF]
, 2011 We establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models. The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components ...
Caffo, Brian S +5 more
core +1 more source
Pregnancy, Volume 2, Issue 2, March 2026.Abstract Objective To identify patterns of gestational weight gain (pGWG) trajectories in the first 20 weeks of gestation and to determine the association of these patterns with the delivery of large‐for‐gestational‐age (LGA) infants among women with insulin‐dependent diabetes mellitus (IDDM).
Ketrell L. McWhorter +8 more
wiley +1 more source
Dynamic functional principal components [PDF]
, 2012 In this paper, we address the problem of dimension reduction for sequentially observed functional data (X_k : k ∈ Z). Such functional time series arise frequently, e.g., when a continuous time process is segmented into some smaller natural units, such
Hallin, Marc +2 more
core
PLoS ONE, 2019 PurposeThis study investigated the relationship between the ground reaction force-time profile of a countermovement jump (CMJ) and fatigue, specifically focusing on predicting the onset of neuromuscular versus metabolic fatigue using the CMJ.MethodTen ...
Paul Pao-Yen Wu +5 more
doaj +1 more source
Functional additive regression
, 2015 We suggest a new method, called Functional Additive Regression, or FAR, for efficiently performing high-dimensional functional regression. FAR extends the usual linear regression model involving a functional predictor, $X(t)$, and a scalar response, $Y$,
Fan, Yingying +2 more
core +1 more source
Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse and Fragmented Functional Data [PDF]
, 2020 Mathematical and Physical Sciences: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)In many applications, smooth processes generate data that is recorded under a variety of observation regimes, such as dense sampling and ...
Matuk, James
core
Degradation modeling applied to residual lifetime prediction using functional data analysis
, 2011 Sensor-based degradation signals measure the accumulation of damage of an engineering system using sensor technology. Degradation signals can be used to estimate, for example, the distribution of the remaining life of partially degraded systems and/or ...
Gebraeel, Nagi +2 more
core +1 more source
Ekonometria, 2019 The functional principal components analysis joins the advantages of the principal components analysis and provide analysis of dynamic data. The main difference in both methods is the type of data the PCA is based on multivariate data, whereas the FPCA ...
Mirosława Sztemberg-Lewandowska
doaj
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
Functional principal component analysis for incomplete space–time data
Environmental and Ecological StatisticsEnvironmental signals, acquired, e.g., by remote sensing, often present large gaps of missing observations in space and time. In this work, we present an innovative approach to identify the main variability patterns, in space–time data, when data may be ...
Alessandro Palummo +3 more
semanticscholar +1 more source