Results 61 to 70 of about 17,400 (155)
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
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
, 2015 An important endpoint variable in a cocaine rehabilitation study is the time to first relapse of a patient after the treatment. We propose a joint modeling approach based on functional data analysis to study the relationship between the baseline ...
Guan, Yongtao, Li, Yehua, Ye, Jun
core +1 more source
Statistics in Medicine, Volume 45, Issue 6-7, March 2026.ABSTRACT Factor analysis (FA) can be used to identify key biomarkers in biological processes by assuming that latent biological pathways (statistically, “latent factors”) drive the activity of measurable biomarkers (“observed variables”). However, biological pathways often interact, meaning that the classical FA assumption of independence between ...
Jiachen Cai +2 more
wiley +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
Principal Nested Spheres for Time Warped Functional Data Analysis [PDF]
, 2013 There are often two important types of variation in functional data: the horizontal (or phase) variation and the vertical (or amplitude) variation. These two types of variation have been appropriately separated and modeled through a domain warping method
Lu, Xiaosun, Marron, J. S.
core
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
Advances in Modal Regression: From Theoretical Foundations to Practical Implementations
WIREs Computational Statistics, Volume 18, Issue 1, March 2026.Modal regression. ABSTRACT Modal regression has emerged as a powerful alternative to traditional mean and quantile regression by focusing on estimating the conditional mode rather than the conditional mean or quantiles. This approach offers robustness to outliers and skewed distributions while providing more informative prediction intervals for ...
Sijia Xiang, Weixin Yao, Xinping Cui
wiley +1 more source
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
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.ABSTRACT Technological advancements in wearable devices and medical imaging often lead to high‐dimensional physiological signals in the form of images or surfaces. To address these data structures, we develop a novel survival on image regression model with a specific focus on partially functional distributional representation of wearable data.
Rahul Ghosal +2 more
wiley +1 more source