Parametric modelling of cost data: some simulation evidence
Parametric modelling of cost data: some simulation evidence
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DOI：
10.1002/hec.941
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发表时间：
2005-04-01
期刊：
影响因子：
2.1
通讯作者：
Thompson, S
中科院分区：
文献类型：
--
作者：
Briggs, A;Nixon, R;Thompson, S
最近，评论员建议，应明确地模拟成本数据的分布形式，以提高估计总体平均数的效率。我们进行了一系列的模拟实验，以评估通常的样本均值和对数正态分布的均值估计，在理论分布和三个大型经验数据集的背景下。样本均值总是无偏的，但当总体分布真的是对数正态分布时，它的有效性稍低。然而，当真实分布不是对数正态分布时，对数正态估计量的表现令人震惊。在实际情况下，真实分布是未知的，样本平均值通常仍然是选择的估计，特别是当有限的样本大小禁止详细的成本数据分布建模。版权所有（c）2005年约翰威利父子有限公司。
Recently, commentators have suggested that the distributional form of cost data should be explicitly modelled to gain efficiency in estimating the population mean. We perform a series of simulation experiments to evaluate the usual sample mean and the mean estimator of a lognormal distribution, in the context of both theoretical distributions and three large empirical datasets. The sample mean is always unbiased, but is somewhat less efficient when the population distribution is truly lognormal. However the lognormal estimator can perform appallingly when the true distribution is not lognormal. In practical situations, where the true distribution is unknown, the sample mean generally remains the estimator of choice, especially when limited sample size prohibits detailed modelling of the cost data distribution. Copyright (c) 2005 John Wiley & Sons, Ltd.