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Modeling for Categorical Data and Handling Over-dispersion via Computer Intensive Methods

通过计算机密集方法对分类数据进行建模并处理过度分散

基本信息

批准号：
12680319
负责人：
OCHI Yoshimichi
金额：
$ 1.66万
依托单位：
Oita University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2002
项目状态：
已结题

项目摘要

Possibilities and performances of the use of computer intensive methods for handling over-dispersion are studied when we analyze effects of covariates on multi (including more than three) category response data. We set up a system to generate Dirichlet-multinominal random numbers based on the uniform random numbers given by a physical random number generator, using beta-binomial decomposition of the Dirichlet-multinominal distribution. Using this system, we carried out simulation studies for examining effects of handling over-dispersion regarding covariate effect evaluation in the categorical data analysis. Especially we focus on elucidating effectiveness of computer intensive methods such as bootstrap and jackknife methods, compared to the alternatives like multinomial maximum likelihood method (ML), Dirichlet-multinominal ML or generalized estimating equations (GEE) based on their moments up to 2nd order. The simulation results showed the computer intensive methods were comparable to the Dirichlet-multinominal ML, even in the case where the latter is the optimum for the study setting. Furthermore, it is indicated that the computer intensive methods are robust to the departure of the postulated models from the true structure.Based on these simulation results, we applied above methods to some real data and we found that there was a case where the Dirichlet-multinominal ML failed to fit the data while the GEE and the computer intensive methods were able to fit the data successfully.Since the calculations of the computational intensive methods themselves are costly, the simulation studies to evaluate their performances require substantial amount of computational burden. We therefore investigated efficient use of computational resources to ease this problem with simultaneous use of several computers via distributed parallel processing methods. This issue needs to be explored further.

研究了在分析协变量对多类（包括三类以上）反应数据的影响时，使用计算机密集型方法处理过度离差的可能性和性能。我们建立了一个系统来产生Dirichlet-多项式随机数的基础上，均匀随机数的物理随机数发生器，使用β-二项式分解的Dirichlet-多项式分布。使用这个系统，我们进行了模拟研究，以检查处理过度分散的影响，关于协变量效应评估的分类数据分析。特别是，我们专注于阐明计算机密集型的方法，如自助法和刀切法的有效性，相比的替代品，如多项式最大似然法（ML），狄利克雷多项式ML或广义估计方程（GEE）的基础上，他们的时刻到第二阶。模拟结果表明，计算机密集型的方法是可比的狄利克雷多项式ML，即使在后者是最佳的研究设置的情况下。此外，计算机密集型方法对假设模型与真实结构的偏离具有鲁棒性。基于这些模拟结果，我们将上述方法应用于一些真实的数据，我们发现存在Dirichlet-多项式ML未能拟合数据，而GEE和计算机密集型方法能够成功拟合数据。密集型方法本身是昂贵的，模拟研究，以评估其性能需要大量的计算负担。因此，我们研究了有效利用计算资源，以缓解这个问题，同时使用几台计算机通过分布式并行处理方法。这个问题需要进一步探讨。