POWRE: Problems in Classification and Clustering

POWRE：分类和聚类问题

• 批准号: 9806070
• 负责人: Olga Cordero-Brana
• 金额: $ 9.45万
• 依托单位: American University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-15 至 1999-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9806070&HistoricalAwards=false
• 关键词: POWRE; Problems; Classification; Clustering

Mixtures distributions have been used extensively as models in situations where data are viewedas arising from a population that is comprised of several subpopulations mixed in varying proportions. Applications of mixture models covers a wide variety of fields: remote sensing, agriculture, electrophoresis, medical diagnosis and prognosis, earth studies, sampling, population diversity, marketing research, image enhancing and picture coding, human and animal perception, economics, genetics, electrophoresis to name afew.The research will focus on three major areas. First,it will extend the work of Cordero Brana on minimum Hellinger distance estimation from univariate mixture models to the multivariate case. This will involve investigating and deriving an appropriate multivariate density estimator for mixture models as well as considering the computational aspects of it. Also convergence properties of the HMIX algorithm and theadaptive density estimator will be studied. The advantage of this method is that it produces estimates thatare both efficient and robust to the presence of data contamination unlike maximum likelihood methods.Second, the applicant will consider BRM-IS algorithm for computing posterior distributions. The method is similar to the Weighted Likelihood Bootstrap (WLB) procedure of Newton and Raftery (1994) but BRM-IS is better in the case of incomplete data since it takes into account the prior distribution of the parameters of interest. BRM has been successfully applied to censored Weibull models, failure models with Weibull distributions and to (univariate) mixtures of mixture distributions.The plan is to carry out this work with Dr. Gilles Celeux and his research team at the National Institute for Research in Computer Science and Control (INRIA Rhone-Alpes, France).This POWRE project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).>

混合分布已被广泛用作模型的情况下，数据被视为从一个人口，是由几个不同比例的亚群混合。 混合模型的应用范围非常广泛，包括遥感、农业、电泳、医学诊断和预后、地球研究、采样、种群多样性、市场研究、图像增强和图像编码、人类和动物感知、经济学、遗传学、电泳等。 首先，它将扩展的Cordero Brana的工作最小Hellinger距离估计从单变量混合模型到多变量的情况下。这将涉及调查和推导一个适当的多元密度估计的混合模型，以及考虑它的计算方面，也将研究HMIX算法和自适应密度估计的收敛性。 这种方法的优点是，它产生的估计是有效的和鲁棒的数据污染的存在不同的最大似然方法。第二，申请人将考虑BRM-IS算法计算后验分布。该方法类似于Newton和Raftery（1994）的加权Likestival Bootstrap（WLB）程序，但BRM-IS在不完整数据的情况下更好，因为它考虑了感兴趣的参数的先验分布。BRM已成功地应用于截尾威布尔模型，威布尔分布的失效模型，（单变量）混合分布的混合。计划是与Gilles Celeux博士和他在国家计算机科学与控制研究所的研究小组一起开展这项工作（INRIA罗纳-阿尔佩斯，法国）。该POWRE项目由MPS多学科活动办公室（OMA）和数学科学部（DMS）联合支持。>