Quantile Estimation with Adaptive Importance Sampling

Quantile Estimation with Adaptive Importance Sampling

Extreme quantile estimation with nonparametric adaptive importance sampling

In this article, we propose a nonparametric adaptive importance sampling (NAIS) algorithm to estimate rare event quantile. Indeed, Importance Sampling (IS) is a well-known adapted random simulation technique. It consists in generating random weighted samples from an auxiliary distribution rather than the distribution of interest. The optimization of this auxiliary distribution is often very dif...

Quantile Estimation with Latin Hypercube Sampling

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Quantile Estimation in Successive Sampling

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion o...

Quantile Estimation from Ranked Set Sampling Data

We consider estimation of quantiles when data are generated from ranked set sampling. A new estimator is proposed and is shown to have a smaller asymptotic variance for all distributions. It is also shown that the optimal sampling strategy is to select observations with one fixed rank from different ranked sets. Both the optimal rank and the relative efficiency gain with respect to simple rando...