Foundations of Statistical Inference: Proceedings of the Shoresh Conference 2000 [Paperback]

This volume is a collection of papers presented at a conference held in Shoresh Holiday Resort near Jerusalem, Israel, in December 2000 organized by the Israeli Ministry of Science, Culture and Sport. The theme of the conference was Foundation of Statistical Inference: Applications in the Medical and Social Sciences and in Industry and the Interface of Computer Sciences . The following is a quotation from the Program and Abstract booklet of the conference. Over the past several decades, the field of statistics has seen tremendous growth and development in theory and methodology. At the same time, the advent of computers has facilitated the use of modern statistics in all branches of science, making statistics even more interdisciplinary than in the past; statistics, thus, has become strongly rooted in all empirical research in the medical, social, and engineering sciences. The abundance of computer programs and the variety of methods available to users brought to light the critical issues of choosing models and, given a data set, the methods most suitable for its analysis. Mathematical statisticians have devoted a great deal of effort to studying the appropriateness of models for various types of data, and defining the conditions under which a particular method work. In 1985 an international conference with a similar title* was held in Is? rael. It provided a platform for a formal debate between the two main schools of thought in Statistics, the Bayesian, and the Frequentists.I. Identification with Incomplete Observations, Data Mining.- Bounding Entries in Multi-way Contingency Tables Given aSet of Marginal Totals.- Identification and Estimation with Incomplete Data.- Computational Information Retrieval.- Studying Treatment Response to Inform Treatment Choice.- II. Bayesian Methods and Modelling.- Some Interactive Decision Problems Emerging in Statistical Games.- Probabilistic Modelling: An Historical andPhilosophical Digression.- A Bayesian View on Sampling thelc*