Research on an integration methodology for combining many competing models with a dynamical mixing and/or switching

我们研究了经验贝叶斯过程和伪贝叶斯过程的集成方法，重点研究了选择最佳元模型的信息准则，每个元模型都是通过组合多个竞争模型来提高预测能力的。我们在序贯蒙特卡罗方法的框架下，致力于发展新的重采样方法，该方法在贝叶斯计算中引起了广泛的兴趣。通过研究人工智能领域中的模型平均方法和Boost算法在算法上的相似性，本文还进行了理论研究。我们考虑了一个过程的应用，在这个过程中，许多竞争模型的混合和/或切换机制在提高预测能力和灵活性方面发挥了重要作用，同时保持了鲁棒性。对于具体的应用实例，我们进行了…信号自动处理的研究项目这些技术包括人造卫星、高频金融数据分析、视觉和传感器融合技术的自动目标跟踪和识别、销售点(POS)数据分析以及GPS网络数据与时间相关的反演。我们通过将该方法应用于实际问题，证实了所提出的自动创建模型的算法的有效性，并针对社会需求攻击了新的具有挑战性的问题。特别是，我们开发了一种新的DNA阵列数据分析技术，可以在基因组科学的单个实验中测量数千个基因的表达水平。在DNA阵列数据的分析中，我们面临的困难大多源于这样一个事实，即尽管病例数量(样本量)至多为100个，但特征向量(基因数量)的维度从几千到几万不等。因此，由于过度学习，不可能应用传统的聚类方法。为了克服这些困难，我们开发了一种新的聚类方法，并提供了一个名为ArrayCluster的软件，可以从http://www.ism.ac.jp/〜higuchi/arraycluster.htm.下载我们组织了一个关于统计建模方法论进步的国际研讨会--《建模科学：信息标准(AIC2003)30周年》，以促进建模科学的发展。较少

We studied an integration method of the empirical Bayesian and pseudo-Bayesian procedures and concentrated on developing information criteria to choose the best meta-model each of which is built up by combining many competing models to enhance a performance on the predictive ability. We made an effort to develop the new methods for resampling procedures in a framework of the sequential Monte Carlo methods that has drawn many interests in Bayesian computation. A theoretical work has been also studied by investigating the similarities in algorithms between the model averaging approach and boosting in Artificial Intelligence community. We considered an application of the procedures in which a mixing and/or switching mechanisms of many competing models plays an important role in boosting the predictive ability as well as the flexibility while keeping the robustness. As for the concrete examples of application, we have conducted the research projects on an automatic transaction of signals o … More btained by the artificial satellites, an analysis of high-frequent financial data, automatic target tracking and recognition for vision and sensor fusion technique, analysis of Point of Sales (POS) data, and time-dependent inversion from GPS network data. We confirmed an effectiveness of the proposed algorithms for an automatic creation of models by applying this methodology to real world problems, and attacked the new challenging problems in response to the social demand. In particular, we developed the new analysis technique for DNA array data that measures the expression level of thousands of genes in a single experiment in genome science. In the analysis of DNA array data, we are faced with difficulties most of which arise from the fact such that while the number of cases (sample size) is at most 100, a dimension of the feature vector (number of genes) ranges from several thousands up to a few ten thousands. It is therefore impossible to apply the conventional clustering methods, because of over-learning. To overcome such difficulty, we developed a novel clustering method, and provided a software called ArrayCluster which can be downloaded at http://www.ism.ac.jp/〜higuchi/arraycluster.htm. We organized an international symposium on advancement in the statistical modeling methodology, "Science of Modeling : The 30^<th> Anniversary of the Information Criterion (AIC2003)" for promoting the Science of Modeling. Less