Multiscale Change Point Inference

Frick, Klaus; Munk, Axel; Sieling, Hannes

doi:10.1111/rssb.12047

Cited by 308 publications

(522 citation statements)

References 149 publications

(293 reference statements)

Supporting

Mentioning

514

Contrasting

Order By: Relevance

“…We compared FPOP to several other segmentation algorithms: pDPA (Rigaill 2010), PELT (Killick et al 2012), Binary Segmentation (BinSeg), Wild Binary Segmentation (WBS; Fryzlewicz 2012), and SMUCE (Frick et al 2014). We ran pDPA and BinSeg with a maximum number of changes K = 52, WBS and SMUCE with default settings, and PELT and FPOP with the SIC penalty.…”

Section: Speed Benchmark: 4467 Chromosomes From Tumour Microarraysmentioning

confidence: 99%

“…However, functional pruning is computationally more demanding than inequality based pruning. We thus decided to empirically compare the performance of FPOP to PELT (Killick et al 2012), pDPA (Rigaill 2010), Binary Segmentation (BinSeg), Wild Binary Segmentation (WBS) (Fryzlewicz 2012) and SMUCE (Frick et al 2014).…”

Section: Empirical Evaluation Of Fpopmentioning

confidence: 99%

“…As before the data is then split around the changepoint and the process repeated on the two created segments. Lastly SMUCE (Simultaneous Multiscale Changepoint Inference) (Frick et al 2014) uses a multiscale test at level α and estimates a step function that minimises the number of changepoints whilst lying in the acceptance region of this test.…”

Section: Empirical Evaluation Of Fpopmentioning

confidence: 99%

“…There are a wide-range of approaches to detecting changepoints, see for example Frick et al (2014) and Aue and Horvth (2013) and the references therein. We focus on one important class of approaches (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On optimal multiple changepoint algorithms for large data

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Speed Benchmark: 4467 Chromosomes From Tumour Microarraysmentioning

confidence: 99%

Section: Empirical Evaluation Of Fpopmentioning

confidence: 99%

Section: Empirical Evaluation Of Fpopmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On optimal multiple changepoint algorithms for large data

et al. 2016

View full text Add to dashboard Cite

show abstract

“…See for example Yao (1988), Lavielle (2005), Killick et al (2012) and Davis et al (2006). Finally, the segmentation of the data is obtained as the one that minimises a penalised version of this cost (see also Frick et al 2014, for an extension of these approaches).…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient nonparametric approach for changepoint detection

2016

View full text Add to dashboard Cite

In this paper we build on an approach proposed by for nonparametric changepoint detection. This approach defines the best segmentation for a data set as the one which minimises a penalised cost function, with the cost function defined in term of minus a non-parametric loglikelihood for data within each segment. Minimising this cost function is possible using dynamic programming, but their algorithm had a computational cost that is cubic in the length of the data set. To speed up computation, resorted to a screening procedure which means that the estimated segmentation is no longer guaranteed to be the global minimum of the cost function. We show that the screening procedure adversely affects the accuracy of the changepoint detection method, and show how a faster dynamic programming algorithm, pruned exact linear time (PELT) (Killick et al. 2012), can be used to find the optimal segmentation with a computational cost that can be close to linear in the amount of data. PELT requires a penalty to avoid under/over-fitting the model which can have a detrimental effect on the quality of the detected changepoints. To overcome this issue we use a relatively new method, changepoints over a range of penalties (Haynes et al. 2016), which finds all of the optimal segmentations for multiple penalty values over a continuous range. We apply our method to detect changes in heart-rate during physical activity. Electronic supplementary materialThe online version of this article

show abstract

Single Molecule Data Analysis: An Introduction

Tavakoli

Taylor

et al. 2017

Advances in Chemical Physics

View full text Add to dashboard Cite

Multiscale Change Point Inference

Cited by 308 publications

References 149 publications

On optimal multiple changepoint algorithms for large data

On optimal multiple changepoint algorithms for large data

A computationally efficient nonparametric approach for changepoint detection

Single Molecule Data Analysis: An Introduction

Contact Info

Product

Resources

About