ArticleComputational statistics & data analysis2019
Robust probabilistic classification applicable to irregularly sampled functional data.
Article in Computational statistics & data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Robust domain selection for functional data via interval-wise testing and effect size mapping.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Robust Inference for Partially Observed Functional Response Data.Statistica Sinica · 2022Article
- A Phantom-Based Assessment of Repeatability and Reproducibility of Transvaginal Quantitative Ultrasound.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
A robust probabilistic classifier for functional data is developed to predict class membership based on functional input measurements and to provide a reliable probability estimates for class membership. The method combines a Bayes classifier and semi-parametric mixed effects model with robust tuning parameter to make the method robust to outlying curves, and to improve the accuracy of the risk or uncertainty estimates, which is crucial in medical diagnostic applications. The approach applies to functional data with varying ranges and irregular sampling without making parametric assumptions on the within-curve covariance. Simulation studies evaluate the proposed method and competitors in terms of sensitivity to heavy tailed functional distributions and outlying curves. Classification performance is evaluated by both error rate and logloss, the latter of which imposes heavier penalties on highly confident errors than on less confident errors. Runtime experiments on the R implementation indicate that the proposed method scales well computationally. Illustrative applications include data from quantitative ultrasound analysis and phoneme recognition.
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