Results 51 to 60 of about 1,173 (164)
Optimal Estimation of Large Functional and Longitudinal Data by Using Functional Linear Mixed Model
Mathematics, 2022 The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the most challenging problems in the field of high ...
Mengfei Ran, Yihe Yang
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Advanced Photonics Research, Volume 7, Issue 5, May 2026.This review critically examines clinical studies on both conventional and machine learning (ML)‐integrated diffuse optical spectroscopy and imaging methods for dermatological applications, with a primary focus on the past decade and inclusion of earlier foundational work where appropriate.
Iftak Hussain +7 more
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Health Prognostics in Multi‐Sensor Systems Based on Multivariate Functional Data Analysis
Applied Stochastic Models in Business and Industry, Volume 42, Issue 3, May/June 2026.ABSTRACT Recent developments in big data analysis, machine learning, Industry 4.0, and IoT applications have enabled the monitoring and processing of multi‐sensor data collected from systems, allowing for the prediction of the “Remaining Useful Life” (RUL) of system components.
Cevahir Yildirim +2 more
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A Hybrid Nonparametric Framework for Outlier Detection in Functional Time Series
Environmetrics, Volume 37, Issue 4, May 2026.ABSTRACT Outlier detection in functional time series is challenging due to temporal dependence and the simultaneous presence of magnitude, shape, and partial anomalies. Existing methods often assume independence or rely on model based approaches, such as the Standard Smoothed Bootstrap on Residuals (SmBoR), which may not work well if the model is ...
David Solano +4 more
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Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, 2015 Analiza głównych składowych (PCA) polega na transformacji zmiennych pierwotnych w zbiór nowych wzajemnie ortogonalnych zmiennych, zwanych głównymi składowymi.
Małgorzata Sej-Kolasa +1 more
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FPCA-based Method to Select Optimal Sampling Schedules That Capture Between-subject Variability in Longitudinal Studies [PDF]
Biometrics, 2017 Summary A critical component of longitudinal study design involves determining the sampling schedule. Criteria for optimal design often focus on accurate estimation of the mean profile, although capturing the between-subject variance of the longitudinal process is also important since variance patterns may be associated with covariates ...
Meihua Wu +3 more
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A New Approach to Statistical Inference for Functional Time Series
Journal of Time Series Analysis, Volume 47, Issue 3, Page 675-686, May 2026.ABSTRACT The analysis of time‐indexed functional data plays an important role in the field of business and economic statistics. In the literature, statistical inference for functional time series often involves reducing the dimension of functional data to a finite dimension K$$ K $$, followed by the use of tools from multivariate analysis.
Hanjia Gao, Yi Zhang, Xiaofeng Shao
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Palaeontology, Volume 69, Issue 3, 2026.Abstract The Triassic was a unique time for beak evolution, as seen in a wide diversity of terrestrial tetrapods. Beaks were present in dicynodont synapsid survivors of the Permo‐Triassic mass extinction event (PTME) and evolved independently several times in archosauromorphs and their relatives.
Damiano Landi +4 more
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Oil and the stock market revisited: A mixed functional VAR approach
Quantitative Economics, Volume 17, Issue 2, Page 541-589, May 2026.This paper proposes a new mixed vector autoregression (MVAR) model to examine the relationship between aggregate time series and functional variables in a multivariate setting. The model facilitates a reexamination of the oil‐stock price nexus by estimating the effects of demand and supply shocks from the global market for crude oil on the entire ...
Hilde C. Bjørnland +2 more
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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
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