eprintid: 4992
rev_number: 23
eprint_status: archive
userid: 351
dir: disk0/00/00/49/92
datestamp: 2016-01-15 02:08:07
lastmod: 2021-08-27 17:15:49
status_changed: 2016-01-15 02:08:07
type: monograph
metadata_visibility: show
item_issues_count: 2
creators_name: Pflug, G.C.
creators_id: 1361
creators_orcid: 0000-0001-8215-3550
title: Metric Entropy and Nonasymptotic Confidence Bands in Stochastic Programming
ispublished: pub
internal_subjects: iis_frc
internal_subjects: iis_met
divisions: prog_opt
abstract: Talagrand has demonstrated in his key paper, how the metric entropy of a class of functions relates to uniform bounds for the law of large numbers. This paper shows how to calculate the metric entropy of classes of functions which appear in stochastic optimization problems. As a consequence of these results, we derive via variational inequalities confidence bands for the solutions, which are valid for any sample size. In particular, the linear recourse problem is considered.
date: 1996-04
date_type: published
publisher: WP-96-034
iiasapubid: WP-96-034
price: 10
creators_browse_id: 229
full_text_status: public
monograph_type: working_paper
place_of_pub: IIASA, Laxenburg, Austria
pages: 14
coversheets_dirty: FALSE
fp7_type: info:eu-repo/semantics/book
citation:   Pflug, G.C. <https://pure.iiasa.ac.at/view/iiasa/229.html> ORCID: https://orcid.org/0000-0001-8215-3550 <https://orcid.org/0000-0001-8215-3550>  (1996).  Metric Entropy and Nonasymptotic Confidence Bands in Stochastic Programming.   IIASA Working Paper. IIASA, Laxenburg, Austria: WP-96-034     
document_url: https://pure.iiasa.ac.at/id/eprint/4992/1/WP-96-034.pdf