2009 Workshop on Semiparametric methodology

2009年半参数方法研讨会

• 批准号: 0838278
• 负责人: Michael Daniels
• 金额: --
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-11-01 至 2009-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0838278&HistoricalAwards=false
• 关键词: 2009; Workshop; Semiparametric; methodology

Semiparametric methods continue to be an active research area,especially as computing resources and power grow. These methods are usedfor inference in models with both parametric and nonparametriccomponents. Work in this setting ranges from semiparametric modeling andefficiency to semiparametric regression. The former involves makingefficient inferences in the presence of infinite dimensional nuisanceparameters. The latter corresponds to regression models with parametricerrors and unspecified (nonparametric) regression functions. Fundamentalwork remains to be done in high dimensional missing data problems, bothin the setting of frequentist and Bayesian inference and in estimatingregression functions with the complications posed in many differentareas of application. The workshop brings together leaders in the fieldof semiparametric methodology and young researchers. The invitedspeakers all are very distinguished individuals who have doneoutstanding work in semiparametrics and have co-authored three of theseminal texts. Flexible modeling with few assumptions is an integral component ofsolving many scientific problems. Semiparametric methods are a way toaccomplish this. Major advances have been made in the past thirty years.However, fundamental work remains to be done in high dimensionalproblems with incomplete data, in estimating regression functions withthe complications posed in many different areas of application, amongother areas. Applications for such work are wide ranging and includespatio-temporal models, capture-recapture models, and measurement errormodels in environmental applications, classification and testing in ingenetics and genomics, and missing data models in longitudinalobservational studies and clinical trials. The workshop provides anexcellent opportunity to discuss the many recent significantdevelopments in semiparametric methodology and to identify importantproblems and new research directions.

半参数方法仍然是一个活跃的研究领域，尤其是随着计算资源和功率增长。这些方法用于具有参数和非参数components的模型中的推断。在这种环境中的工作范围从半参数建模和效率到半参数回归。前者涉及在存在无限尺寸的nuisanceParameter的情况下进行有效的推论。后者对应于带有参数序列的回归模型和未指定的（非参数）回归函数。在高维缺失的数据问题中，基本工作仍有待完成，在许多应用程序中，频繁主义者和贝叶斯推断的设置以及估算复杂性的估算函数的估算函数。该研讨会汇集了半参数方法和年轻研究人员领域的领导者。被邀请的言论者都是非常杰出的人，他们在半乳头上进行了工作，并与人合着了三本thessinal文本。具有很少假设的灵活建模是解决许多科学问题的组成部分。半参数方法是解决此问题的一种方式。在过去的三十年中，已经取得了重大进展。但是，在估计回归功能的高维问题中，基本工作仍有待完成的基本工作，并在其他领域的许多不同领域中构成并发症。此类工作的应用是广泛的范围，包括周期性模型，捕获征用模型以及在环境应用中的测量错误模型，在Ingenetics and Genomics中的分类和测试中，以及在纵向单位研究和临床试验中缺少数据模型。该研讨会为讨论半理解方法的许多最新重大开发提供了挑战的机会，并确定重要问题和新的研究方向。