In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance reduction. And our stratified estimator is asymptotically normal with asymptotic variance e...

High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer databas...

In this article, we propose several quantization based stratified sampling methods to reduce the variance of a Monte-Carlo simulation. Theoretical aspects of stratification lead to a strong link between the problem of optimal Lquantization of a random variable and the variance reduction that can be achieved. We first emphasize on the consistency of quantization for designing strata in stratifie...

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to u...

We develop a methodology for evaluating a decision strategy generated by a stochastic optimization model. The methodology is based on a pilot study in which we estimate the distribution of performance associated with the strategy, and define an appropriate stratified sampling plan. An algorithm we call filtered search allows us to implement this plan efficiently. We demonstrate the approach’s a...

SUMMARY Multivariate normal or Student importance sampling is a commonly used technique for integration problems in statistical inference. This integration approach is easy to implement, has straightforward error estimates and is eeective in a number of problems. A variety of variance reduction techniques can be considered with importance sampling. Stratiied sampling is one of these and in fact...

This paper addresses the problem of estimating the coverage of a fault tolerance mechanism through statistical processing of observations collected in fault-injection experiments. In an earlier paper, several techniques for sampling the fault/activity input space of a fault tolerance mechanism were presented. Various estimators based on simple sampling in the whole space and stratified sampling...

We look at the benefits of using a kind of quasi-random numbers to obtain more accurate results for a given number of simulation runs. We explore a sampling method with enhanced independence in multidimensional simulations by combining the ideas of stratified sampling and Latin Hypercube sampling. We test the new sampling method by comparing it with traditional stratified sampling and Latin Hyp...