Mathematical Sciences:Regional Conference on "Bayesian Methods in Finite Population Sampling Theory"

9312931 Gelfand本次会议的主题是有限总体抽样中的贝叶斯方法：理论和应用。贝叶斯技术在统计学的几乎每一个分支中都有应用，并被证明在分析和解释广泛的数据方面很有用。贝叶斯分析除了利用收集的数据中的信息外，还利用了先验信息，特别适合于有限总体抽样。在大多数调查中，都有一些关于要抽样的人口单位的先验信息。例如，为了根据来自大量农业单位的样本估计某种作物的产量，通常可以获得所有这些单位中专门用于该特定作物的面积的信息。贝叶斯方法使人们能够以先验分布的形式正式地结合这种先验信息，并以相当直接的方式产生估计。此外，越来越需要产生可靠的小面积估计，即对县、市和其他当地地区的估计。传统的抽样调查方法通常被设计为在更高的聚合水平上提供有效的估计，例如在州一级。但是，仅基于给定局部区域的数据的标准估计通常由于局部区域内的小样本大小而导致不可接受的大的标准误差。贝叶斯方法能够借入实力，或使用来自类似地区或来源的信息来提高个别局部地区估计的准确性。这是用于公共部门和私营部门正在收集的各种调查数据。会议将提供一个机制来综合贝叶斯有限总体抽样一般领域的大量和多样化的文献，并将用一些实际应用来说明这些方法。***

9312931 Gelfand The subject matter of this conference is Bayesian Methods in Finite Population Sampling: Theory and Applications. Bayesian techniques have found applications in virtually every branch of statistics, and have proved to be useful in the analysis and interpretation of a wide range of data. Bayesian analysis which utilizes prior information in addition to the information from the collected data, is particularly well-suited in finite population sampling. In most surveys, there is some prior information available about the units of the population to be sampled. For example, for estimating the yield of a certain crop based on samples from a large number of agricultural units, information is usually available regarding the area devoted to that particular crop in all these units. Bayesian methods enable one to incorporate this prior information formally in the form of a prior distribution, and produce estimates in a fairly straight forward manner. In addition, there is a growing need to produce reliable small-area estimates, that is estimates for counties, municipalities, and other local areas. Classical sample survey methods are usually designed to provide effective estimates at a much higher level of aggregation, for example at the state level. But standard estimation based only upon data for a given local area typically results in unacceptably large standard errors due to small sample size within a local area. Bayesian methods enable borrowing strength, or use information from similar areas or sources to improve accuracy of individual local area estimates. This is used for a wide range of survey data being collected by both the public and the private sectors. The Conference will provide a mechanism for synthesizing the substantial and diverse literature in the general area of Bayesian finite population sampling, and will illustrate the methods with a number of real life applications. ***