We study the problem of empirical minimization for variance-type functionals over functional classes. Sharp non-asymptotic bounds excess variance are derived under mild conditions. In particular, it is shown that some restrictions imposed on class fast convergence rates can be achieved including optimal non-parametric expressive classes in non-Donsker regime additional assumptions. Our main app...

When the times between renewals in a renewal process are not exponentially distributed, simulation can become a viable method of analysis. The renewal function is estimated through simulation for a renewal process simulation for a renewal process with gamma distributed renewal times and the shape parameter a > 1. Gamma random deviates will be generated by means of the so called Acceptance Rejec...

Abstract We introduce a general framework that constructs estimators with reduced variance for random walk Metropolis and Metropolis-adjusted Langevin algorithms. The resulting require negligible computational cost are derived in post-process manner utilising all proposal values of the Variance reduction is achieved by producing control variates through approximate solution Poisson equation ass...

Radar meteorology applications rely on pulsed weather radars that survey the atmosphere and estimate a set of meteorological variables for each resolution volume. The reliability of these applications depends on the quality of the supplied data. To reduce the statistical uncertainty of estimates of spectral moments and polarimetric variables for each resolution volume, weather radars often aver...

This paper gives an overview of those aspects of simulation methodology that are (to some extent) peculiar to the simulation of queueing systems. A generalized semi-Markov process framework for describing queueing systems is used through much of the paper. The main topics covered are: output analysis for simulation of transient and steady-state quantities, variance reduction methods that exploi...

In this tutorial three experienced instructors describe how they teach an advanced simulation topic: Poisson processes (Henderson), variance reduction (Jacobson), and verification and validation (Robinson). 1 HENDERSON: POISSON PROCESSES

In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.

We design a variance reduction method to reduce the estimation error in regression problems. It is based on an appropriate use of other known regression functions. Theoretical estimates are supporting this improvement and numerical experiments are illustrating the efficiency of the method.