文章基本信息

标题：Whether Gaussian Nucleus Entropy Helps? Case in Point is Prediction of Number of Cesarean Births
本地全文：下载
作者：Shanmugam, Ramalingam
期刊名称：American Journal of Biostatistics
印刷版ISSN：1948-9889
电子版ISSN：1948-9897
出版年度：2016
卷号：6
期号：2
页码：20-29
DOI：10.3844/amjbsp.2016.20.29
出版社：Science Publications
摘要：In this article, entropy in the collected data about the Gaussian population mean is traced from its embryonic stage as new data are periodically collected. The traditional Shannon's entropy has shortcomings from the data analytics point of view and it creates a necessity to refine the Shannon's entropy . Its refined version is named Gaussian Nucleus Entropy in this article. Advantages of the refined version are pointed out. The Prior, likelihood, Posterior and predictive nucleus entropies are derived, interconnected and interpreted. The results are illustrated using data on cesarean births in thirteen countries in the period [1987, 2007]. The medical communities and families are alarmed, as the cesarean births are increasing not due to emergency or necessity basis but rather for monetary or convenience basis. Nucleus entropy based data analysis answers whether their alarm is baseless.
关键词：Prior; Posterior and Predictive Densities; Bayes Risk; Hypothesis Testing; Power