ArticleStatistica Sinica2022
Robust Inference for Partially Observed Functional Response Data.
Article in Statistica Sinica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 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
- Testing Linear Operator Constraints in Functional Response Regression with Incomplete Response Functions.Electronic journal of statistics · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitative ultrasound signal analysis, this paper investigates a class of robust M-estimators for partially observed functional data including functional location and quantile estimators. Consistency of the estimators is established under general conditions on the partial observation process. Under smoothness conditions on the class of M-estimators, asymptotic Gaussian process approximations are established and used for large sample inference. The large sample approximations justify a bootstrap approximation for robust inferences about the functional response process. The performance is demonstrated in simulations and in the analysis of irregular functional data from quantitative ultrasound analysis.
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Registered trials
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