English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Inference for empirical Wasserstein distances on finite spaces.

Sommerfeld, M., & Munk, A. (2018). Inference for empirical Wasserstein distances on finite spaces. Journal of the Royal Statistical Society. Ser. B, Statistical Methodology, 80(1), 219-238. doi:10.1111/rssb.12236.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-9BF1-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-B9BF-6
Genre: Journal Article

Files

show Files
hide Files
:
2518668.pdf (Publisher version), 3MB
 
File Permalink:
-
Name:
2518668.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:
-
:
2518668_Suppl.pdf (Supplementary material), 255KB
Name:
2518668_Suppl.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Sommerfeld, M., 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: Bootstrap; Central limit theorem; Directional Hadamard derivative; Hypothesis testing; Optimal transport; Wasserstein distance
 Abstract: The Wasserstein distance is an attractive tool for data analysis but statistical inference is hindered by the lack of distributional limits. To overcome this obstacle, for probability measures supported on finitely many points, we derive the asymptotic distribution of empirical Wasserstein distances as the optimal value of a linear programme with random objective function. This facilitates statistical inference (e.g. confidence intervals for sample-based Wasserstein distances) in large generality. Our proof is based on directional Hadamard differentiability. Failure of the classical bootstrap and alternatives are discussed. The utility of the distributional results is illustrated on two data sets.

Details

show
hide
Language(s): eng - English
 Dates: 2017-05-182018-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1111/rssb.12236
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of the Royal Statistical Society. Ser. B, Statistical Methodology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 80 (1) Sequence Number: - Start / End Page: 219 - 238 Identifier: -