Paper Info
Title
Consistency of sample estimates of risk averse stochastic programs
Abstract
In this paper we study asymptotic consistency of law invariant convex risk measures and the corresponding risk averse stochastic programming problems for independent, identically distributed data. Under mild regularity conditions, we prove a law of large numbers and epiconvergence of the corresponding statistical estimators. This can be applied in a straightforward way to establish convergence with probability 1 of sample-based estimators of risk averse stochastic programming problems.
Year
Venue
Keywords
2013
JOURNAL OF APPLIED PROBABILITY
Law invariant convex and coherent risk measures,stochastic programming,law of large numbers,consistency of statistical estimators,epiconvergence,sample average approximation
Field
DocType
Volume
Econometrics,Convergence (routing),Mathematical optimization,Law of large numbers,Regular polygon,Invariant (mathematics),Risk aversion,Statistics,Independent identically distributed,Stochastic programming,Mathematics,Estimator
Journal
50
Issue
ISSN
Citations 
2
0021-9002
3
PageRank 
References 
Authors
0.45
1
1
Name
Order
Citations
PageRank
Alexander Shapiro11273147.62