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

研究将多个竞争模型与动态混合和/或切换相结合的集成方法

• 批准号: 14208025
• 负责人: HIGUCHI Tomoyuki
• 金额: $ 22.71万
• 依托单位: The Institute of Statistical Mathematics
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (A)
• 财政年份: 2002
• 资助国家: 日本
• 起止时间: 2002 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14208025/
• 关键词: Model averaging; Markov switching; DNA microarray; target tracking; Data mining; high frequency financial data; physical random number; データマイニング

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

我们研究了经验贝叶斯和伪贝叶斯过程的集成方法，并集中于开发信息标准来选择最佳元模型，每个元模型都是通过组合许多竞争模型来建立的，以提高预测能力的性能。我们努力在顺序蒙特卡罗方法的框架中开发重采样过程的新方法，这引起了贝叶斯计算的许多兴趣。通过研究人工智能社区中模型平均方法和提升算法之间的相似性，还研究了理论工作。我们考虑了该程序的应用，其中许多竞争模型的混合和/或切换机制在提高预测能力以及灵活性同时保持鲁棒性方面发挥着重要作用。具体应用实例包括人造卫星信号自动交易、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与识别、销售点（POS）数据分析、GPS网络数据时变反演等研究项目。我们通过将该方法应用于现实世界的问题，证实了所提出的自动创建模型算法的有效性，并根据社会需求解决了新的具有挑战性的问题。特别是，我们开发了用于 DNA 阵列数据的新分析技术，可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是由于案例数量（样本量）最多为100个，而特征向量（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。为了克服这个困难，我们开发了一种新颖的聚类方法，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。我们组织了一次关于统计建模方法进展的国际研讨会“建模科学：信息标准30周年纪念日（AIC2003）”，以促进建模科学的发展。较少的

项目成果

状態空間モデルを用いた飲食店売上の要因分解

使用状态空间模型对餐厅销售额进行因子分解

• 发表时间: 2004
• 期刊: オベレーションズ・リサーチ誌 49
• 作者: 山口類

初動が緩慢な波動現象開始時点の精密同定:Pi2型地磁気脈動オンセットタイムの決定法

慢初动波浪现象起始点的精确识别：Pi2型地磁脉动起始时间的确定方法

• 发表时间: 2004
• 期刊: 電子情報通信学会和文論文誌 Vol.J87-A No.12
• 作者: 福山恵子;樋口知之;魚住禎司;河野英昭;湯元清文
• 通讯作者: 湯元清文

G.Kitagawa, T.Higuchi, S.Sato: "Computational methods for time series analysis"Proceedings of Computational Statitstics 2002. 1-10 (2002)

G.Kitakawa、T.Higuchi、S.Sato：《时间序列分析的计算方法》Proceedings of Computational Statistics 2002. 1-10 (2002)

S.Imoto, T.Higuchi, T.Goto, K.Tashiro, S.Kuhara, S.Miyano: "Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks"Proceedings of IEEE Computer Society Bioinformatics Conference. 104-113 (2003)

S.Imoto、T.Higuchi、T.Goto、K.Tashiro、S.Kuhara、S.Miyano：“结合微阵列和生物知识通过贝叶斯网络估计基因网络”IEEE 计算机学会生物信息学会议论文集。

A mixed factors model for dimension reduction and extraction of a group structure in gene expression data

• DOI: 10.1109/csb.2004.13
• 发表时间: 2004-08
• 期刊: Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.
• 作者: Ryo Yoshida;T. Higuchi;S. Imoto