A regularized decomposition method for minimizing a sum of polyhedral functions
Mathematical Programming: Series A and B
Asymptotic analysis of stochastic programs
Annals of Operations Research
Asymptotic theory for solutions in statistical estimation and stochastic programming
Mathematics of Operations Research
Variance of the sample mean: properties and graphs of quadratic-form estimators
Operations Research
Strong consistency of the variance estimator in steady-state simulation output analysis
Mathematics of Operations Research
A branch and bound method for stochastic global optimization
Mathematical Programming: Series A and B
On the relationship between batch means, overlapping means and spectral estimation
WSC '87 Proceedings of the 19th conference on Winter simulation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Overlapping batch means: something for nothing?
WSC '84 Proceedings of the 16th conference on Winter simulation
Assessing solution quality in stochastic programs
Mathematical Programming: Series A and B
Jackknife estimators for reducing bias in asset allocation
Proceedings of the 38th conference on Winter simulation
Reformulation and sampling to solve a stochastic network interdiction problem
Networks - Games, Interdiction, and Human Interaction Problems on Networks
Overlapping Variance Estimators for Simulation
Operations Research
Efficient Computation of Overlapping Variance Estimators for Simulation
INFORMS Journal on Computing
Sample average approximation of expected value constrained stochastic programs
Operations Research Letters
Monte Carlo bounding techniques for determining solution quality in stochastic programs
Operations Research Letters
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We investigate the use of overlapping batches for assessing solution quality in stochastic programs. Motivated by the original use of overlapping batches in simulation, we present a variant of the multiple replications procedure that reuses data via variably overlapping batches to obtain alternative variance estimators. These estimators have lower variances, where the degree of variance reduction depends on the amount of overlap. We provide several asymptotic properties and present computational results to examine small-sample behavior.