Bayesian optimal cluster designs

Q Mathematics

Statistical Methodology Pub Date : 2016-09-01 DOI:10.1016/j.stamet.2016.02.002

Satya Prakash Singh, Siuli Mukhopadhyay

引用次数: 8

Abstract

Designing cluster trials depends on the knowledge of the intracluster correlation coefficient. To overcome the issue of parameter dependence, Bayesian designs are proposed for two level models with and without covariates. These designs minimize the variance of the treatment contrast under certain cost constraints. A pseudo Bayesian design approach is advocated that integrates and averages the objective function over a prior distribution of the intracluster correlation coefficient. Theoretical results on the Bayesian criterion are noted when the intracluster correlation follows a uniform distribution. Two data sets based on educational surveys conducted in schools are used to illustrate the proposed methodology.

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贝叶斯最优聚类设计

设计聚类试验取决于对聚类内相关系数的了解。为了克服参数依赖的问题，提出了带协变量和不带协变量的两级模型的贝叶斯设计。这些设计在一定的成本限制下使处理对比的差异最小化。提倡一种伪贝叶斯设计方法，在聚类内相关系数的先验分布上对目标函数进行集成和平均。当簇内相关性服从均匀分布时，贝叶斯准则的理论结果是显著的。本文使用基于学校教育调查的两组数据来说明所建议的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Methodology STATISTICS & PROBABILITY-

CiteScore

0.59

自引率

0.00%

发文量

期刊介绍： Statistical Methodology aims to publish articles of high quality reflecting the varied facets of contemporary statistical theory as well as of significant applications. In addition to helping to stimulate research, the journal intends to bring about interactions among statisticians and scientists in other disciplines broadly interested in statistical methodology. The journal focuses on traditional areas such as statistical inference, multivariate analysis, design of experiments, sampling theory, regression analysis, re-sampling methods, time series, nonparametric statistics, etc., and also gives special emphasis to established as well as emerging applied areas.