One of the fundamental of mathematical statistics is the estimation of sampling characteristics of a random variable, a problem that is increasingly solved using bootstrap methods. Often these involve Monte Carlo simulation, but they may be costly and time-consuming in certain problems. Various methods for reducing the simulation cost in bootstrapp simulations have been proposed, most of them applicable to simple random samples. Here we review the literature on efficient resampling methods, make comparisons, try to assess the best method for a particular problem.

Efficient boostrap methods: a review

Gigli Anna
1996

Abstract

One of the fundamental of mathematical statistics is the estimation of sampling characteristics of a random variable, a problem that is increasingly solved using bootstrap methods. Often these involve Monte Carlo simulation, but they may be costly and time-consuming in certain problems. Various methods for reducing the simulation cost in bootstrapp simulations have been proposed, most of them applicable to simple random samples. Here we review the literature on efficient resampling methods, make comparisons, try to assess the best method for a particular problem.
1996
Istituto di Ricerche sulla Popolazione e le Politiche Sociali - IRPPS
Monte Carlo methods
Importance sampling
Balanced sampling
Control variate method
Antithetic variates
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/62110
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