ArticleBiometrics2013
Corrected confidence bands for functional data using principal components.
Article in Biometrics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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
49 citing papers in PubMed.
- Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.The international journal of behavioral nutrition and physical activity · 2024Trial
- Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.Statistics in medicine · 2026Article
- Loss of salivary agglutinin induces changes in the salivary microbiome and accelerates development of oral cancer.Microbiome · 2026Article
- Fast Bayesian Functional Principal Components Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.Statistica Sinica · 2025Article
- Prediction intervals and bands with improved coverage for functional data under noisy discrete observation.Journal of applied statistics · 2025Article
- Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference.Journal of the American Statistical Association · 2025Article
- A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.Statistics in medicine · 2024Article
- A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior.Journal of agricultural, biological, and environmental statistics · 2023Article
- A functional model for studying common trends across trial time in eye tracking experiments.Statistics in biosciences · 2023Article
- Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records.Journal of the American Statistical Association · 2023Article
- A classification for complex imbalanced data in disease screening and early diagnosis.Statistics in medicine · 2022Article
- Modeling continuous glucose monitoring (CGM) data during sleep.Biostatistics (Oxford, England) · 2022Article
- COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques.Computer methods and programs in biomedicine · 2021Article
- Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.Statistics in medicine · 2021Article
- CAUSAL MEDIATION ANALYSIS FOR SPARSE AND IRREGULAR LONGITUDINAL DATA.The annals of applied statistics · 2021Article
- A depth-based global envelope test for comparing two groups of functions with applications to biomedical data.Statistics in medicine · 2021Article
- Nonnegative decomposition of functional count data.Biometrics · 2020Article
- Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Simultaneous confidence corridors for mean functions in functional data analysis of imaging data.Biometrics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN.
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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.