ArticleStatistics and computing2017
On optimal multiple changepoint algorithms for large data.
Article in Statistics and computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed.
- ProteoCast: a web server to predict, validate, and interpret missense variant effects.Journal of molecular biology · 2026Article
- A Fast Multiple Change-Point Detection Method via Generalized Nearly Isotonic Optimization.Entropy (Basel, Switzerland) · 2026Article
- Proteome-wide prediction of the functional impact of missense variants with ProteoCast.Nature communications · 2026Article
- The nuclear cap-binding complex regulates subcellular RNA processing and surveillance of coding and noncoding RNAs.Nucleic acids research · 2026Article
- Genome-wide profiling of unmodified DNA using methyltransferase-directed tagging and enrichment.Cell reports methods · 2025Article
- Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease.NeuroImage · 2024Article
- Assessment of small strain modulus in soil using advanced computational models.Scientific reports · 2023Article
- DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection.NAR genomics and bioinformatics · 2023Article
- Quantifying uncertainty in spikes estimated from calcium imaging data.Biostatistics (Oxford, England) · 2023Article
- Epidemic changepoint detection in the presence of nuisance changes.Statistical papers (Berlin, Germany) · 2023Article
- Testing for a Change in Mean After Changepoint Detection.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Article
- Multithreshold change plane model: Estimation theory and applications in subgroup identification.Statistics in medicine · 2021Article
- Fast nonconvex deconvolution of calcium imaging data.Biostatistics (Oxford, England) · 2020Article
- Optimized variable selection via repeated data splitting.Statistics in medicine · 2020Article
- EXACT SPIKE TRAIN INFERENCE VIA ℓThe annals of applied statistics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many common approaches to detecting changepoints, for example based on statistical criteria such as penalised likelihood or minimum description length, can be formulated in terms of minimising a cost over segmentations. We focus on a class of dynamic programming algorithms that can solve the resulting minimisation problem exactly, and thus find the optimal segmentation under the given statistical criteria. The standard implementation of these dynamic programming methods have a computational cost that scales at least quadratically in the length of the time-series. Recently pruning ideas have been suggested that can speed up the dynamic programming algorithms, whilst still being guaranteed to be optimal, in that they find the true minimum of the cost function. Here we extend these pruning methods, and introduce two new algorithms for segmenting data: FPOP and SNIP. Empirical results show that FPOP is substantially faster than existing dynamic programming methods, and unlike the existing methods its computational efficiency is robust to the number of changepoints in the data. We evaluate the method for detecting copy number variations and observe that FPOP has a computational cost that is even competitive with that of binary segmentation, but can give much more accurate segmentations.
Indexed as
Identifiers
What OpenQuestion holds
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.