Sequential Monte Carlo methods for statistical analysis of tables
Sequential Monte Carlo methods for statistical analysis of tables
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DOI：
10.1198/016214504000001303
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发表时间：
2005-03-01
影响因子：
3.7
通讯作者：
Liu, JS
中科院分区：
文献类型：
--
作者：
Chen, YG;Diaconis, P;Liu, JS
我们描述了一个顺序重要性抽样（SIS）程序，用于分析具有固定边际金额的双向0 - 1或列联表。新方法的一个基本特点是按一定的特殊分布对表的列进行逐级抽样。我们的方法产生非常接近均匀分布的蒙特卡罗样本，使人们能够近似这些表的各种检验统计量的零分布。我们的方法优于其他现有的基于蒙特卡罗的算法，有时效率要高几个数量级。特别是，与基于马尔可夫链蒙特卡罗（MCMC）的方法相比，我们的重要抽样方法不仅在绝对运行时间方面更有效率，而且可以避免考虑混合问题，而且可以轻松准确地估计具有固定边际和的表的总数，这是MCMC方法难以实现的。
We describe a sequential importance sampling (SIS) procedure for analyzing two-way zero-one or contingency tables with fixed marginal sums. An essential feature of the new method is that it samples the columns of the table progressively according to certain special distributions. Our method produces Monte Carlo samples that are remarkably close to the uniform distribution, enabling one to approximate closely the null distributions of various test statistics about these tables. Our method compares favorably with other existing Monte Carlo-based algorithms, and sometimes is a few orders of magnitude more efficient. In particular, compared with Markov chain Monte Carlo (MCMC)-based approaches, our importance sampling method not only is more efficient in terms of absolute running time and frees one from pondering over the mixing issue, but also provides an easy and accurate estimate of the total number of tables with fixed marginal sums, which is far more difficult for an MCMC method to achieve.