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和伪贝叶斯过程的集成方法，并重点研究了选择最佳元模型的信息标准，每个元模型都是由多个竞争模型组合而成的，以提高预测能力。我们努力在顺序蒙特卡罗方法的框架中开发重新采样过程的新方法，该方法在贝叶斯计算中引起了许多兴趣。通过对人工智能领域模型平均方法与boosting算法的相似性进行了理论研究。我们考虑了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同时保持鲁棒性方面发挥重要作用的过程的应用。在具体应用实例方面，我们开展了人工卫星获取的o . o . More信号自动处理、高频金融数据分析、基于视觉与传感器融合技术的自动目标跟踪与识别、销售点数据分析、GPS网络数据时变反演等研究项目。通过将该方法应用于现实世界的问题，我们证实了所提出算法在自动创建模型方面的有效性，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来自这样一个事实，即当案例数（样本量）最多为100时，特征向量（基因数）的一个维度从几千到几万不等。因此，由于过度学习，传统的聚类方法是不可能应用的。为了克服这一困难，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，该软件可以在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进步的国际研讨会，“建模科学：信息标准30周年（AIC2003）”，以促进建模科学。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