Results 51 to 60 of about 1,020,465 (280)

Function-on-function linear quantile regression

Mathematical Modelling and Analysis, 2022
In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
doaj   +1 more source

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, Ana M. Aguilera, Manuel Escabias   +2 more
doaj   +1 more source

Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study. [PDF]

PLoS ONE, 2008
BACKGROUND: Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas.
Susanne Hartmann, John H Missimer, Cornelia Stoeckel, Eugenio Abela, Jon Shah, Rüdiger J Seitz, Bruno J Weder   +6 more
doaj   +1 more source

Multiple Change-Point Detection in a Functional Sample via the 𝒢-Sum Process

Mathematics, 2022
We first define the G-CUSUM process and investigate its theoretical aspects including asymptotic behavior. By choosing different sets G, we propose some tests for multiple change-point detections in a functional sample.
Tadas Danielius, Alfredas Račkauskas
doaj   +1 more source

Copula Dynamic Conditional Correlation and Functional Principal Component Analysis of COVID-19 Mortality in the United States

Axioms, 2022
This paper shows a visual analysis and the dependence relationships of COVID-19 mortality data in 50 states plus Washington, D.C., from January 2020 to 1 September 2022.
Jong-Min Kim
doaj   +1 more source

MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR HIGH-DIMENSIONAL DATA [PDF]

, 2010
We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits. The proposed inferential methods avoid the difficult task of loading the entire data set at once in the ...
Caffo, Brian, Crainiceanu, Ciprian, Davatzikos, Christos, Schwartz, Brian S., Yousem, David M., Zipunnikov, Vadim   +5 more
core   +2 more sources

Sparse multivariate functional principal component analysis

Stat, 2022
We introduce a sparse multivariate functional principal component analysis method by incorporating ideas from the group sparse maximum variance method to multivariate functional data. Our method can avoid the “curse of dimensionality” from a high‐dimensional dataset and enjoy interpretability at the same time. In particular, our unsupervised method can
Jun Song, Kyongwon Kim
openaire   +2 more sources

Patient‐Level Barriers and Facilitators to Inpatient Physical Therapy in Adolescents and Young Adults With a Hematological Malignancy: A Qualitative Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Despite their increased risk for functional impairment resulting from cancer and its treatments, few adolescents and young adults (AYAs) with a hematological malignancy receive the recommended or therapeutic dose of exercise per week during inpatient hospitalizations.
Jennifer A. Kelleher, Jennifer L. Bernstein, Katia M. Crisler, Julia K. Herriott, Kimberly L. Klages, Robin E. Norris, Mariann L. Strenk, Sarah E. Bills, Meghan E. McGrady   +8 more
wiley   +1 more source

Developmental Disorders in Children Recently Diagnosed With Cancer

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Neurocognitive deficits in adult survivors of childhood cancer are well established, but less is known about developmental disorders (DD) arising shortly after cancer diagnosis. Using 2016–2019 linked Ohio cancer registry and Medicaid data, we compared DD among 324 children with cancer and 606,913 cancer‐free controls.
Jamie Shoag, Long Vu, Ritika Miryala, Jeffrey M. Albert, Molly McVoy, Siran M. Koroukian   +5 more
wiley   +1 more source

Functional principal component analysis for cointegrated functional time series

Journal of Time Series Analysis, 2023
Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool.
openaire   +2 more sources