English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Autocovariance estimation in regression with a discontinuous signal and m-dependent errors: A difference-based approach.

Tecuapetla-Gomez, I., & Munk, A. (2017). Autocovariance estimation in regression with a discontinuous signal and m-dependent errors: A difference-based approach. Scandinavian Journal of Statistics, 44(2), 346-368. doi:10.1111/sjos.12256.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-592A-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-592E-7
Genre: Journal Article

Files

show Files
hide Files
:
2450841.pdf (Publisher version), 2MB
 
File Permalink:
-
Name:
2450841.pdf
Description:
-
Visibility:
Restricted (Max Planck Institute for Biophysical Chemistry (Karl Friedrich Bonhoeffer Institute), MBPC; )
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-
:
2450841_Suppl.pdf (Supplementary material), 266KB
Name:
2450841_Suppl.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Tecuapetla-Gomez, I., 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: autocovariance estimation; change-point; convex projection; covariance matrix estimation; difference-based methods; discontinuous signal; m-dependent processes; mean squared error; non-parametric regression
 Abstract: We discuss a class of difference-based estimators for the autocovariance in nonparametric regression when the signal is discontinuous and the errors form a stationary m-dependent process. These estimators circumvent the particularly challenging task of pre-estimating such an unknown regression function. We provide finite-sample expressions of their mean squared errors for piecewise constant signals and Gaussian errors. Based on this, we derive biased-optimized estimates that do not depend on the unknown autocovariance structure. Notably, for positively correlated errors, that part of the variance of our estimators that depend on the signal is minimal as well. Further, we provide sufficient conditions for root n-consistency; this result is extended to piecewise Holder regression with non-Gaussian errors. We combine our biased-optimized autocovariance estimates with a projection-based approach and derive covariance matrix estimates, a method that is of independent interest. An R package, several simulations and an application to biophysical measurements complement this paper.

Details

show
hide
Language(s): eng - English
 Dates: 2016-11-162017-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1111/sjos.12256
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Scandinavian Journal of Statistics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 44 (2) Sequence Number: - Start / End Page: 346 - 368 Identifier: -