推动当代统计学的极限：为纪念Jayanta K。Ghosh
Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh
摘要：
Jayanta Kumar Ghosh是统计学领域最杰出的教授之一。他在许多领域的研究，特别是渐近，已经突破性的，在世界各地有影响力，并通过奖项和其他荣誉得到广泛认可。他作为印度统计研究所所长和国际统计研究所所长以及其他重要职位在统计方面的领导作用也同样出色。为了表彰贾扬塔的巨大影响，本卷是一个努力，以表彰他通过绘制贡献的主要领域，他已经工作，并继续工作。论文自然分为五类。首先，序贯估计是Jayanta的出发点。因此，从这个主题开始，有两篇论文，一个经典的霍尔和丁导致一个变量的p值，和一个贝叶斯的伯杰和孙扩展参考先验停时的问题。第二，在一般的先验规范领域，有五篇论文。Jayanta早期的大部分工作都涉及群体家庭，比如Sweeting的论文。也有两个文件居住之间的联系模糊集和先验，由Meeden和Delampady和昂热。同样大胆的是Mukerjee的工作，他利用数据依赖的先验知识，以及Ghosh、Santra和Kim发现的几个先验选择标准的令人满意的融合。Jayanta自己研究了各种先验选择标准，包括概率匹配先验和参考先验。
英文摘要：
Jayanta Kumar Ghosh is one of the most extraordinary professors in the field of Statistics. His research in numerous areas, especially asymptotics, has been groundbreaking, influential throughout the world, and widely recognized through awards and other honors. His leadership in Statistics as Director of the Indian Statistical Institute and President of the International Statistical Institute, among other eminent positions, has been likewise outstanding. In recognition of Jayanta's enormous impact, this volume is an effort to honor him by drawing together contributions to the main areas in which he has worked and continues to work. The papers naturally fall into five categories. First, sequential estimation was Jayanta's starting point. Thus, beginning with that topic, there are two papers, one classical by Hall and Ding leading to a variant on p-values, and one Bayesian by Berger and Sun extending reference priors to stopping time problems. Second, there are five papers in the general area of prior specification. Much of Jayanta's earlier work involved group families as does Sweeting's paper here for instance. There are also two papers dwelling on the link between fuzzy sets and priors, by Meeden and by Delampady and Angers. Equally daring is the work by Mukerjee with data dependent priors and the pleasing confluence of several prior selection criteria found by Ghosh, Santra and Kim. Jayanta himself studied a variety of prior selection criteria including probability matching priors and reference priors.
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