Optimised importance sampling quantile estimation
Optimised importance sampling quantile estimation
复制标题
优化重要性采样分位数估计
DOI：
10.1093/biomet/83.4.791
复制
发表时间：
1996
期刊：
影响因子：
2.7
通讯作者：
D. Wallach
中科院分区：
文献类型：
--
作者：
B. Goffinet;D. Wallach
本文考虑利用辅助变量X* 估计检验统计量X的分位数，X* 可以是X的渐近展开式，也可以是忽略某些协方差结构的简化形式。建议的估计包括三个阶段。首先，绘制一个大样本，并评估X*。然后，绘制第一子样本，评估X和X*，并估计给定X* 的X的条件分布。然后，第二子样本被绘制为具有针对该条件分布和特定分位数优化的权重。从这个子样本中，得到分位数的重要抽样估计。所得到的估计被证明有显着较低的均方误差比传统的估计，并合理稳健的条件分布和假设的优化分位数的模型中的错误。举一个遗传学的例子。
This paper considers the use of an auxiliary variable X* to estimate quantiles of a test statistic X; X* may be an asymptotic expansion of X, or a simplified version which ignores some of the covariance structure. The proposed estimator involves three stages. First a large sample is drawn, and X* is evaluated. Then a first subsample is drawn, X and X* are evaluated and the conditional distribution of X given X* is estimated. Then a second subsample is drawn with weighting which is optimised for this conditional distribution, and for a particular quantile. From this subsample, an importance sampling estimator of the quantile is obtained. The resulting estimator is shown to have substantially lower mean squared error than the conventional estimator, and to be reasonably robust both to errors in the model for the conditional distribution and to the quantile assumed for the optimisation. An example in genetics is given.