Research on Sieve Approximations to Non and Semiparametric Models, Hidden Markov Models and Comparison of Phylogenetic Tree Biologies

非参数和半参数模型的筛逼近、隐马尔可夫模型以及系统发育树生物学比较的研究

• 批准号: 9802960
• 负责人: Peter Bickel
• 金额: $ 29.02万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-01 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9802960&HistoricalAwards=false
• 关键词: Research; Sieve; Approximations; Non; Semiparametric

----------------------------------------------------------------------- Proposal Number: DMS-9802261 PI: Peter Bickel Institution: Project: Research on sieve approximations to non and semiparametric models, Hidden Markov models and comparison of phylogenetic tree biologies. Abstract: A theoretical investigation of the "plug in" property in the context of non and semiparametric models. The intention of the investigators is to characterize non and semiparametric models in which the outcomes of appropriate fitting procedures can be safely plugged in for a broad range of uses, and the study of model selection criteria when the loss function reflects the goal of fitting some features of the data well rather than a global fit. This project includes: *Further development of a theory for testing parametric or semiparametric hypotheses in a semi or non parametric context. *Further development of the theory of inference for Hidden Markov Models. The investigators propose extension to state space models. *Development of new procedures and analysis of existing procedures for estimating fixed effects and prediction of random effects using semiparametric models for longitudinal and/or "pharmacokinetic" data. *Further development of the theory and practice of selecting m in the m out of n bootstrap *An examination of the sensitivity to choice of stochastic model in the construction of phylogenetic trees Tests and diagnostics for semiparametric models such as those the investigators intend to continue to develop are useful in a number of areas. For instance, the Black Scholes option pricing formula is widely used in finance. One of the methods the investigators have already developed show the invalidity of the formula for large data set and points to plausible more realistic models. Phylogenetic trees are used not only for representing evolutionary relationships among species of animals and plants but also, as in the case we are g oing to study, important families of proteins. Studying the types of models that lead to plausible evolution trees should also lead to pattern recognition algorithms which will be useful in classifying protein families and hence to relating new proteins to families whose properties are known. This is a major activity in the search for new bioactive compounds in biotechnology.

- 提案编号：DMS-9802261 PI：Peter Bickel机构： 项目：研究非参数和半参数的筛分近似 模型、隐马尔可夫模型和系统树比较 生物学 摘要： 非参数和半参数模型中“插入”性质的理论研究。 调查人员的意图是， 描述非参数和半参数模型，其中 适当的装配程序可以安全地插入， 使用范围，以及研究损失时的模型选择标准 函数反映了很好地拟合数据的某些特征的目标 而不是全局匹配。 该项目包括： * 进一步发展测试参数或 半参数或非参数背景下的半参数假设。 * 隐马尔可夫推理理论的进一步发展 模型 研究人员提出了对状态空间模型的扩展。 * 制定新程序和分析现有程序， 估计固定效应和预测随机效应 纵向和/或“药代动力学”数据的半参数模型。 * 进一步发展在n中取m自助法中选择m的理论和实践 * 在构建系统发育树时对随机模型选择的敏感性的检查 半参数模型的检验和诊断，例如 调查人员打算继续发展， 地区 例如，Black Scholes期权定价公式为： 广泛用于金融。 研究人员已经开发出的一种方法表明，该公式对大量数据无效 设置并指向看似合理的更现实的模型。 系统发生树不仅用于描述动物和植物物种之间的进化关系，而且，正如我们将要研究的那样，还用于描述重要的蛋白质家族。 研究导致合理进化树的模型类型也应该导致模式识别算法，这将有助于对蛋白质家族进行分类，从而将新蛋白质与其特性已知的家族联系起来。 这是生物技术中寻找新的生物活性化合物的主要活动。