ArticleBiometrics2020
Simultaneous confidence corridors for mean functions in functional data analysis of imaging data.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.Statistica Sinica · 2025Article
- Efficient Bayesian functional principal component analysis of irregularly-observed multivariate curves.Computational statistics & data analysis · 2025Article
- Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference.Journal of the American Statistical Association · 2025Article
- Inverse set estimation and inversion of simultaneous confidence intervals.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer.Biometrics · 2023Article
- Predicting the onset of breast cancer using mammogram imaging data with irregular boundary.Biostatistics (Oxford, England) · 2023Article
- Functional delta residuals and applications to simultaneous confidence bands of moment based statistics.Journal of multivariate analysis · 2022Article
- Simultaneous confidence bands for functional data using the Gaussian Kinematic formula.Journal of statistical planning and inference · 2022Article
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Authors and funding
4 authors.
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Abstract
Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle an irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two-sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyze brain positron emission tomography data in two different studies. One data set used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
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