SEQUENTIAL MONTE CARLO METHODS FOR PERMUTATION TESTS ON TRUNCATED DATA
SEQUENTIAL MONTE CARLO METHODS FOR PERMUTATION TESTS ON TRUNCATED DATA
复制标题
截断数据排列检验的顺序蒙特卡罗方法
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Jun S. Liu
中科院分区：
文献类型：
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作者：
Yuguo Chen;Jun S. Liu
排列检验是最古老的统计推断技术之一。蒙特卡罗方法和渐近公式已用于近似相关的 p 值。然而，当数据被截断时，排列零分布就很难处理。我们在这里描述了一种有效的顺序重要性采样策略，用于生成具有受限位置的排列，该策略在我们测试的所有示例中提供了准确的 p 值近似值。该算法还可以很好地估计零一矩阵的常量，这本身就是一个具有挑战性的问题。我们策略的关键是允许的排列和具有结构零的零一表之间的联系。
The permutation test is one of the oldest techniques for making statis- tical inferences. Monte Carlo methods and asymptotic formulas have been used to approximate the associated p-values. When data are truncated, however, the permutation null distribution is difficult to handle. We describe here an efficient se- quential importance sampling strategy for generating permutations with restricted positions, which provides accurate p-value approximations in all examples we have tested. The algorithm also provides good estimates of permanents of zero-one matri- ces, which by itself is a challenging problem. The key to our strategy is a connection between allowable permutations and zero-one tables with structural zeros.
影响因子：
1.9
作者：
C. McLaren;M. Wagstaff;G. Brittenham;A. Jacobs
通讯作者：
C. McLaren;M. Wagstaff;G. Brittenham;A. Jacobs