English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Heterogeneous change point inference.

Pein, F., Sieling, H., & Munk, A. (2017). Heterogeneous change point inference. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 79(4), 1207-1227. doi:10.1111/rssb.12202.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-3088-7 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-308C-0
Genre: Journal Article

Files

show Files
hide Files
:
2502969.pdf (Publisher version), 2MB
 
File Permalink:
-
Name:
2502969.pdf
Description:
-
Visibility:
Restricted (Max Planck Institute for Biophysical Chemistry (Karl Friedrich Bonhoeffer Institute), Göttingen; )
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-
:
2502969_Suppl.pdf (Supplementary material), 673KB
Name:
2502969_Suppl.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Pein, F., Author
Sieling, H., Author
Munk, A.1, Author              
Affiliations:
1Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society, ou_1113580              

Content

show
hide
Free keywords: Change point regression; Deviation bounds; Dynamic programming; Heterogeneous noise; Honest confidence sets; Ion channel recordings; Multiscale methods; Robustness; Scale-dependent critical values
 Abstract: We propose, a heterogeneous simultaneous multiscale change point estimator called 'H-SMUCE' for the detection of multiple change points of the signal in a heterogeneous Gaussian regression model. A piecewise constant function is estimated by minimizing the number of change points over the acceptance region of a multiscale test which locally adapts to changes in the variance. The multiscale test is a combination of local likelihood ratio tests which are properly calibrated by scale-dependent critical values to keep a global nominal level a, even for finite samples. We show that H-SMUCE controls the error of overestimation and underestimation of the number of change points. For this, new deviation bounds for F-type statistics are derived. Moreover, we obtain confidence sets for the whole signal. All results are non-asymptotic and uniform over a large class of heterogeneous change point models. H-SMUCE is fast to compute, achieves the optimal detection rate and estimates the number of change points at almost optimal accuracy for vanishing signals, while still being robust. We compare H-SMUCE with several state of the art methods in simulations and analyse current recordings of a transmembrane protein in the bacterial outer membrane with pronounced heterogeneity for its states. An R-package is available on line.

Details

show
hide
Language(s): eng - English
 Dates: 2016-08-192017-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1111/rssb.12202
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of the Royal Statistical Society. Series B, Statistical Methodology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 79 (4) Sequence Number: - Start / End Page: 1207 - 1227 Identifier: -