bibtype J - Journal Article
ARLID 0388146
utime 20240103202044.6
mtime 20130122235959.9
title (primary) (eng) Behavior and Convergence of Wasserstein Metric in the Framework of Stable Distributions
specification
page_count 15 s.
serial
ARLID cav_un_epca*0293025
ISSN 1212-074X
title Bulletin of the Czech Econometric Society
volume_id 2012
volume 30 (2012)
page_num 124-138
keyword Wasserstein Metric
keyword Stable Distributions
keyword Empirical Distribution Function
author (primary)
ARLID cav_un_auth*0271480
name1 Omelchenko
name2 Vadym
full_dept (cz) Ekonometrie
full_dept (eng) Department of Econometrics
department (cz) E
department (eng) E
institution UTIA-B
full_dept Department of Econometrics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2013/E/omelchenko-behavior and convergence of wasserstein metric in the framework of stable distributions.pdf
cas_special
project
project_id GAP402/10/0956
agency GA ČR
ARLID cav_un_auth*0263482
research CEZ:AV0Z10750506
abstract (eng) In this paper, we aim to explore the speed of convergence of the Wasserstein distance between stable cumulative distribution functions and their empirical counterparts. The theoretical results are compared with the results provided by simulations. The need to use simulations is explained by the fact that all the theoretical results which relate to the speed of convergence of the Wasserstein Metric in the set-up of stable distributions are asymptotic; therefore, the question of when that theory starts to be valid remains open. The asymptotic results are true only for relatively large numbers of observations exceeding hundreds of thousands. In cases dealing with lower numbers of observations, the speed of convergence turns out to be much slower than we might expect.
reportyear 2013
RIV BB
num_of_auth 1
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0217170
arlyear 2012
mrcbU63 cav_un_epca*0293025 Bulletin of the Czech Econometric Society 1212-074X Roč. 2012 č. 30 2012 124 138