Journal of Computational and Graphical Statistics· 2026Q1
Detection of mean changes in partially observed functional data
- 0citations
- Q1SCImago
- 2026year
Short summary
A new statistical test can detect changes in the mean of functional data even when observations are only available on subsets of the domain.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a test for detecting mean changes in functional data with partial observations.
- Accommodates both abrupt and gradual changes in the data.
- Significance assessed via permutation tests, including a data-driven resampling variant.
- Methodology validated through Monte Carlo simulations and real data application.
AI-generated from the title and abstract; the full text is not read.
Abstract
We propose a test for a change in the mean for a sequence of functional observations that are only partially observed on subsets of the domain, with no information available on the complement. The framework accommodates important scenarios, including both abrupt and gradual changes. The significance of the test statistic is assessed via a permutation test. In addition to the classical permutation approach with a fixed number of permutation samples, we also discuss a variant with controlled resampling risk that relies on a random (data-driven) number of permutation samples. The small sample performance of the proposed methodology is illustrated in a Monte Carlo simulation study and an application to real data.
The authors' abstract, as published at the source. Journal of Computational and Graphical Statistics, 2026 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics