Paper Info
Title
Control Variate Selection For Monte Carlo Integration
Abstract
Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a multiple linear regression model with the integrand as response and the control variates as covariates. Even without special knowledge on the integrand, significant efficiency gains can be obtained if the control variate space is sufficiently large. Incorporating a large number of control variates in the ordinary least squares procedure may however result in (i) a certain instability of the ordinary least squares estimator and (ii) a possibly prohibitive computation time. Regularizing the ordinary least squares estimator by preselecting appropriate control variates via the Lasso turns out to increase the accuracy without additional computational cost. The findings in the numerical experiment are confirmed by concentration inequalities for the integration error.
Year
DOI
Venue
2021
10.1007/s11222-021-10011-z
STATISTICS AND COMPUTING
Keywords
DocType
Volume
Control variate, Lasso, Monte Carlo, Variable selection, Variance reduction
Journal
31
Issue
ISSN
Citations 
4
0960-3174
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Leluc Rémi100.34
Portier, François202.37
Johan Segers34110.37