Estimating the density of a conditional expectation
Estimating the density of a conditional expectation
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估计条件期望的密度
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. Staum
中科院分区：
文献类型：
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作者：
Samuel G. Steckley;S. Henderson;D. Ruppert;Ran Yang;D. Apley;J. Staum
在随机模拟研究的输入模型和参数存在不确定性的情况下，研究的目标往往是对条件期望的估计。条件期望是以所选模型和参数为条件的预期性能。这种条件期望的密度精确而简明地描述了输入不确定性对性能预测的影响。本文利用核密度估计领域的思想来估计条件期望的密度。我们证明了我们的估计量在合理的条件下收敛，并给出了关于最优收敛速度的结果。我们给出了这个估计量的两个修正，一个局部估计量和一个偏差修正估计量。给出了这些估计量的收敛结果。我们研究了我们的估计器在一些测试用例和一个客户到达过程未知的呼叫中心实例上的性能。
Given uncertainty in the input model and parameters of a stochastic simulation study, the goal of the study often becomes the estimation of a conditional expectation. The conditional expectation is expected performance conditioned on the selected model and parameters. The density of this conditional expectation describes precisely, and concisely, the impact of input uncertainty on performance prediction. In this paper we estimate the density of a conditional expectation using ideas from the field of kernel density estimation. We show that our estimator converges under reasonable conditions and present results on optimal rates of convergence. We present two modifications of this estimator, a local estimator and a bias-corrected estimator. Convergence results are given for these estimators. We study the performance of our estimators on a number of test cases and a call center example in which the arrival process of customers is unknown.