Results 41 to 50 of about 2,322 (129)
Plant height defined growth curves can predict end of season maize yield
The Plant Phenome Journal, Volume 8, Issue 1, December 2025.Abstract The development of quick, easy, and low‐cost methods to quantify within‐field variation is essential to successful implementation of mid‐season management for precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict ...
Dorothy D. Sweet +3 more
wiley +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
Clustering Longitudinal Data: A Review of Methods and Software Packages
International Statistical Review, Volume 93, Issue 3, Page 425-458, December 2025.Summary Clustering of longitudinal data is becoming increasingly popular in many fields such as social sciences, business, environmental science, medicine and healthcare. However, it is often challenging due to the complex nature of the data, such as dependencies between observations collected over time, missingness, sparsity and non‐linearity, making ...
Zihang Lu
wiley +1 more source
Statistics in Medicine, Volume 44, Issue 25-27, November 2025.ABSTRACT Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to memory loss, cognitive decline, and behavioral changes, without a known cure. Neuroimages are often collected alongside the covariates at baseline to forecast the prognosis of the patients.
Chun Yin Lee +4 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
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
Deep Mixture of Linear Mixed Models for Complex Longitudinal Data
Statistics in Medicine, Volume 44, Issue 23-24, October 2025.ABSTRACT Mixtures of linear mixed models are widely used for modeling longitudinal data for which observation times differ between subjects. In typical applications, temporal trends are described using a basis expansion, with basis coefficients treated as random effects varying by subject.
Lucas Kock, Nadja Klein, David J. Nott
wiley +1 more source
Bayesian integration of longitudinal and survival outcomes in Alzheimer's disease prediction
Alzheimer's &Dementia, Volume 21, Issue 9, September 2025.Abstract INTRODUCTION Accurate prediction of Alzheimer's disease (AD) dementia onset and progression to mild cognitive impairment (MCI) is crucial for early intervention and clinical trial design. This study presents a predictive framework leveraging Bayesian model averaging (BMA) with a multivariate functional mixed model (MFMM) to integrate ...
Haotian Zou +3 more
wiley +1 more source
PLoS ONE, 2015 BackgroundWastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and ...
Stefania Salvatore +5 more
doaj +1 more source
Intraday forecasts of a volatility index: Functional time series methods with dynamic updating
, 2018 As a forward-looking measure of future equity market volatility, the VIX index has gained immense popularity in recent years to become a key measure of risk for market analysts and academics.
Kearney, Fearghal +2 more
core +1 more source