A new nonparametric procedure designed for simulation studies
A new nonparametric procedure designed for simulation studies
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专为模拟研究设计的新非参数程序
DOI：
10.1080/03610917808812077
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发表时间：
1978
期刊：
影响因子：
--
通讯作者：
J. Marshall
中科院分区：
文献类型：
--
作者：
M. Tarter;J. Marshall
本文演示了如何某些统计，计算样本大小为n（从几乎任何分布）可以模拟使用一个序列大大小于n随机正态变量。许多统计量θ，包括几乎所有的极大似然估计，都可以用样本三角矩STM来表示。STM是渐近多变量正常的平均向量和方差-协方差矩阵很容易表示在等间距的特征函数评价。因此，人们只需要知道傅立叶变换或等效地与任何中等到大i的元素相关联的特征函数。I. D.采样并访问正常随机数生成器以生成具有与从该采样计算的STM的分布特性几乎相同的分布特性的STM序列。这些STM又可以用来计算所需的统计量θ。
This paper demonstrates how certain statistics, computed from a sample of size n (from almost any distribution) may be simulated by using a sequence of substantially less than n random normal variates. Many statistics, θ, including almost all maximum likelihood estimates, can be expressed in terms of the sample trigonometric moments, STM. The STM are asymptotically multivariate normal with a mean vector and variance-covariance matrix easily expressible in terms of equally spaced characteristic function evaluations. Thus one only needs to know the Fourier transform or equivalently the characteristic function associated with elements of any moderate to large i. i. d. sample and have access to a normal random number generator to generate a sequence of STM with distributional properties almost identical to those of STM computed from that sample. These STM can in turn be used to compute the desired statistic θ.