ArticleJournal of the American Statistical Association2018
Modeling motor learning using heteroskedastic functional principal components analysis.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior.Neurorehabilitation and neural repair · 2023Article
- Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.Statistics and its interface · 2022Article
- JOINT AND INDIVIDUAL ANALYSIS OF BREAST CANCER HISTOLOGIC IMAGES AND GENOMIC COVARIATES.The annals of applied statistics · 2021Article
- Smoothness metric during reach-to-grasp after stroke: part 2. longitudinal association with motor impairment.Journal of neuroengineering and rehabilitation · 2021Observational
- Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation.Current neurology and neuroscience reports · 2020Review
- Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.Computational statistics & data analysis · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in motion variance associated with skill learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.