U.S.-India Collaborative Research: Small-area Estimation Problems

美印合作研究：小区域估计问题

• 批准号: 9505197
• 负责人: Parthasarathi Lahiri
• 金额: $ 1.27万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-01 至 1997-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9505197&HistoricalAwards=false
• 关键词: U.S.; India; Collaborative; Research; Small

INT 9505197 Lahiri Technical Description: This project provides partial support for Partha-sarathi Lahiri, University of Nebraska, to conduct statistical research in small-area estimation at the Indian Institute of Management, Calcutta with collaborator Rahul Mukherjee. Dr. Lahiri will also work with T.J. Rao of the Indian Institute of Statistics, Calcutta to explore new areas in survey sampling. Estimators used in large scale national sample surveys perform poorly at the sub-national level. The P.I. proposes to explore the use of empirical Bayes and hierarchical Bayes methods in developing estimators to generate reliable small area statistics. The collaborators will investigate whether this method which Lahiri has shown to be superior to the best linear estimators for small area means, holds for the general mixed model with random error variance components. They will extend the empirical Bayes and hierarchical Bayes methods to find estimators of small area characteristics for discrete data. Scope: Research in small area estimation is important for generating statistics at the state and county levels and for subgroups of the population. In India and the U.S., such statistics are needed by various federal and local governments for policy making and allocation of funds. The foreign collaborator has done outstanding work in the areas of Bayesian statistics, higher order asymptotics and survey sampling. Professor Mukherjee's recent advances in the area of asymptotics will be very helpful in proving difficult asymptotic results mentioned in the proposal. Many mutual benefits may be expected to result from this new collaborative activity. ***

INT 9505197 Lahiri 技术说明：该项目为内布拉斯加大学的 Partha-sarathi Lahiri 提供部分支持，与合作者 Rahul Mukherjee 在加尔各答印度管理学院进行小区域估计统计研究。 Lahiri 博士还将与 T.J.加尔各答印度统计研究所的 Rao 探索调查抽样的新领域。 大规模国家抽样调查中使用的估算器在次国家层面表现不佳。 P.I.建议探索使用经验贝叶斯和分层贝叶斯方法来开发估计器以生成可靠的小区域统计数据。 Lahiri 已证明这种方法优于小面积均值的最佳线性估计器，合作者将研究这种方法是否适用于具有随机误差方差分量的一般混合模型。 他们将扩展经验贝叶斯和分层贝叶斯方法来寻找离散数据小区域特征的估计量。 范围：小区域估计研究对于州和县一级以及人口亚组的统计数据非常重要。 在印度和美国，各个联邦和地方政府都需要此类统计数据来制定政策和分配资金。 国外合作者在贝叶斯统计、高阶渐进和调查抽样等领域做出了杰出的工作。 Mukherjee教授最近在渐近学领域的进展对于证明提案中提到的困难的渐近结果将非常有帮助。 这项新的合作活动有望带来许多互惠互利。 ***