Results 51 to 60 of about 2,322 (129)
Global sensitivity analysis of integrated assessment models with multivariate outputs
Risk Analysis, Volume 45, Issue 8, Page 2132-2156, August 2025.Abstract Risk assessments of complex systems are often supported by quantitative models. The sophistication of these models and the presence of various uncertainties call for systematic robustness and sensitivity analyses. The multivariate nature of their response challenges the use of traditional approaches.
Leonardo Chiani +3 more
wiley +1 more source
Pregnancy, Volume 1, Issue 4, July 2025.Abstract Objective Limited data are available to describe the long‐term implications of pre‐pregnancy diabetes on offspring body composition in adulthood. The objective of this study was to examine the association between maternal glucose control and variation throughout pregnancy and long‐term obesity in offspring.
Katherine Bowers +12 more
wiley +1 more source
Longitudinal Functional Data Analysis
, 2015 We consider analysis of dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated time visits and for each visit we record a functional variable.
Park, So Young, Staicu, Ana-Maria
core +1 more source
A Latent‐Class Model for Time‐To‐Event Outcomes and High‐Dimensional Imaging Data
Statistics in Medicine, Volume 44, Issue 15-17, July 2025.ABSTRACT Structural magnetic resonance imaging (MRI) is one of the primary predictors of Alzheimer's disease risk, enabling the identification of patients with similar risk profiles for precision medicine treatment. Motivated by the need for flexible modeling in AD research, we propose a latent‐class model that addresses the heterogeneity within study ...
Jiahui Feng +4 more
wiley +1 more source
Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach
Statistics in Medicine, Volume 44, Issue 13-14, June 2025.ABSTRACT Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models including many longitudinal markers is still a computational challenge because of the high number of random effects and parameters to be estimated.
Reza Hashemi +3 more
wiley +1 more source
Common Functional Principal Components [PDF]
Functional principal component analysis (FPCA) based on the Karhunen-Lo`eve decomposition has been successfully applied in many applications, mainly for one sample problems.
Alois Kneip +2 more
core
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
Analysis of the Utilization of Machine Learning to Map Flood Susceptibility
Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.ABSTRACT This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial‐and‐error approaches, this work aims at establishing a ...
Ali Pourzangbar +3 more
wiley +1 more source
Revista Colombiana de Estadística, 2007 It is shown how summary statistics of functional data and functional principal components analysis (FPCA) can be used to evaluate the stationarity assumption considered in modeling of regionalized variables.
Giraldo Ramón
doaj