Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Inference for empirical Wasserstein distances on finite spaces.

MPG-Autoren
/persons/resource/persons32719

Munk,  A.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)

2518668_Suppl.pdf
(Ergänzendes Material), 255KB

Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002E-9BF1-3
Zusammenfassung
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.