Ingram Olkin – författare

The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader.Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

For over 200 years, practitioners have been developing parametric families of probability distributions for data analysis. More recently, an active development of nonparametric and semiparametric families has occurred. This book includes an extensive discussion of a wide variety of distribution families—nonparametric, semiparametric and parametric—some well known and some not. An all-encompassing view is taken for the purpose of identifying relationships, origins and structures of the various families. A unified methodological approach for the introduction of parameters into families is developed, and the properties that the parameters imbue a distribution are clarified. These results provide essential tools for intelligent choice of models for data analysis. Many of the results given are new and have not previously appeared in print. This book provides a comprehensive reference for anyone working with nonnegative data.

Statisticians, probabilists, and mathematicians will all be interested in a expanded version of this classic work on inequalities. The theory of inequalities has applications in virtually every branch of mathematics.

A Stanford University Press classic.

Learn How to Infuse Leadership into Your Passion for Scientific Research Leadership and Women in Statistics explores the role of statisticians as leaders, with particular attention to women statisticians as leaders. By paying special attention to women’s issues, this book provides a clear vision for the future of women as leaders in scientific and technical fields. It also shows how emerging and current leaders of both genders in many disciplines can expand their leadership potentials. Featuring contributions from leadership experts and statisticians at various career stages, this unique and insightful text:Examines leadership within the roles of statistician and data scientist from international and diverse perspectivesSupplies a greater understanding of leadership within teams, research consulting, and project managementEncourages reflection on leadership behaviors, promoting both natural and organizational leadershipIdentifies existing opportunities to foster creative outputs and develop strong leadership voicesIncludes real-life stories about overcoming barriers to leadershipLeadership and Women in Statistics explains how to convert a passion for statistical science into visionary, ethical, and transformational leadership. Although the context focuses on statistics, the material applies to almost all fields of endeavor. This book is a valuable resource for those ready to consider leadership as an important element of their careers, and for those who are already leaders but want to deepen their perspectives on leadership. It makes an ideal text for group leadership training as well as for individual professional development.

For over 200 years, practitioners have been developing parametric families of probability distributions for data analysis. More recently, an active development of nonparametric and semiparametric families has occurred. This book includes an extensive discussion of a wide variety of distribution families—nonparametric, semiparametric and parametric—some well known and some not. An all-encompassing view is taken for the purpose of identifying relationships, origins and structures of the various families. A unified methodological approach for the introduction of parameters into families is developed, and the properties that the parameters imbue a distribution are clarified. These results provide essential tools for intelligent choice of models for data analysis. Many of the results given are new and have not previously appeared in print. This book provides a comprehensive reference for anyone working with nonnegative data.

From the Preface: "The theory of optimal design of experiments as we know it today is built on asolid foundation developed by Jack Kiefer, who formulated and resolved some of the major problems of data collection via experimentation. A principal ingredient in his formulation was statistical efficiency of a design. Kiefer's theoretical contributions to optimal designs can be broadly classified into several categories: He rigorously defined, developed, and interrelated statistical notions of optimality. He developed powerful tools for verifying and searching for optimal designs; this includes the "averaging technique"... for approximate or exact theory, and "patchwork"... for exact theory... Kiefer and Wolfowitz provided a theorem now known as the Equivalence Theorem. This result has become a classical theorem in the field. One important feature of this theorem is that it provides a measure of how far a given design is from the optimal design. He characterized and constructed families ofoptimal designs. Some of the celebrated ones are balanced block designs, generalized Youden designs, and weighing designs. He also developed combinatorial structures of these designs. Kiefer's papers are sometimes difficult. In part this is due to the precision and care he exercised, which at times forced a consideration of pathologies and special cases...A reading of his papers on design is replete with examples of his scholarship, his innovativeness, ingenuity, and strength as a researcher."

Learn How to Infuse Leadership into Your Passion for Scientific Research Leadership and Women in Statistics explores the role of statisticians as leaders, with particular attention to women statisticians as leaders. By paying special attention to women’s issues, this book provides a clear vision for the future of women as leaders in scientific and technical fields. It also shows how emerging and current leaders of both genders in many disciplines can expand their leadership potentials. Featuring contributions from leadership experts and statisticians at various career stages, this unique and insightful text:Examines leadership within the roles of statistician and data scientist from international and diverse perspectivesSupplies a greater understanding of leadership within teams, research consulting, and project managementEncourages reflection on leadership behaviors, promoting both natural and organizational leadershipIdentifies existing opportunities to foster creative outputs and develop strong leadership voicesIncludes real-life stories about overcoming barriers to leadershipLeadership and Women in Statistics explains how to convert a passion for statistical science into visionary, ethical, and transformational leadership. Although the context focuses on statistics, the material applies to almost all fields of endeavor. This book is a valuable resource for those ready to consider leadership as an important element of their careers, and for those who are already leaders but want to deepen their perspectives on leadership. It makes an ideal text for group leadership training as well as for individual professional development.

From the Preface: "Jack Kiefer's sudden and unexpected death in August, 1981, stunned his family, friends, and colleagues. Memorial services in Cincinnati, Ohio, Berkeley, California, and Ithaca, New York, shortly after his death, brought forth tributes from so many who shared in his life. But it was only with the passing of time that those who were close to him or to his work were able to begin assessing Jack's impact as a person and intellect. About one year after his death, an expression of what Jack meant to all of us took place at the 1982 annual meeting of the Institute of Mathematical Statistics and the American Statistical Association. Jack had been intimately involved in the affairs of the IMS as a Fellow since 1957, as a member of the Council, as President in 1970, as Wald lecturer in 1962, and as a frequent author in its journals. It was doubly fitting that the site of this meeting was Cincinnati, the place of his birth and residence of his mother, other family, and friends.Three lectures were presented there at a Memorial Session - by Jerry Sacks dealing with Jack's personal life, by Larry Brown dealing with Jack's contributions in statistics and probability, and by Henry Wynn dealing with Jack's contributions to the design of experiments. These three papers, together with Jack's bibliography, were published in the Annals of Statistics and are included as an introduction to these volumes."

From the Preface: "The theory of optimal design of experiments as we know it today is built on asolid foundation developed by Jack Kiefer, who formulated and resolved some of the major problems of data collection via experimentation. A principal ingredient in his formulation was statistical efficiency of a design. Kiefer's theoretical contributions to optimal designs can be broadly classified into several categories: He rigorously defined, developed, and interrelated statistical notions of optimality. He developed powerful tools for verifying and searching for optimal designs; this includes the "averaging technique"... for approximate or exact theory, and "patchwork"... for exact theory... Kiefer and Wolfowitz provided a theorem now known as the Equivalence Theorem. This result has become a classical theorem in the field. One important feature of this theorem is that it provides a measure of how far a given design is from the optimal design. He characterized and constructed families ofoptimal designs. Some of the celebrated ones are balanced block designs, generalized Youden designs, and weighing designs. He also developed combinatorial structures of these designs. Kiefer's papers are sometimes difficult. In part this is due to the precision and care he exercised, which at times forced a consideration of pathologies and special cases...A reading of his papers on design is replete with examples of his scholarship, his innovativeness, ingenuity, and strength as a researcher."

From the Preface: "Jack Kiefer's sudden and unexpected death in August, 1981, stunned his family, friends, and colleagues. Memorial services in Cincinnati, Ohio, Berkeley, California, and Ithaca, New York, shortly after his death, brought forth tributes from so many who shared in his life. But it was only with the passing of time that those who were close to him or to his work were able to begin assessing Jack's impact as a person and intellect. About one year after his death, an expression of what Jack meant to all of us took place at the 1982 annual meeting of the Institute of Mathematical Statistics and the American Statistical Association. Jack had been intimately involved in the affairs of the IMS as a Fellow since 1957, as a member of the Council, as President in 1970, as Wald lecturer in 1962, and as a frequent author in its journals. It was doubly fitting that the site of this meeting was Cincinnati, the place of his birth and residence of his mother, other family, and friends.Three lectures were presented there at a Memorial Session - by Jerry Sacks dealing with Jack's personal life, by Larry Brown dealing with Jack's contributions in statistics and probability, and by Henry Wynn dealing with Jack's contributions to the design of experiments. These three papers, together with Jack's bibliography, were published in the Annals of Statistics and are included as an introduction to these volumes."

This greatly expanded new edition includes recent research on stochastic, multivariate and group majorization, Lorenz order, and applications in physics and chemistry, in economics and political science, in matrix inequalities, and in probability and statistics.

The commentaries in this volume provide reviews of selected papers from the three-volume Collected Papers of Jack Carl Kiefer. From the Preface of Volume III: "The theory of optimal design of experiments as we know it today is built on a solid foundation developed by Jack Kiefer, who formulated and resolved some of the major problems of data collection via experimentation. A principal ingredient in his formulation was statistical efficiency of a design. Kiefer's theoretical contributions to optimal designs can be broadly classified into several categories: He rigorously defined, developed, and interrelated statistical notions of optimality. He developed powerful tools for verifying and searching for optimal designs; this includes the "averaging technique"... for approximate or exact theory, and "patchwork"... for exact theory... Kiefer and Wolfowitz provided a theorem now known as the Equivalence Theorem. This result has become a classical theorem in the field. One importantfeature of this theorem is that it provides a measure of how far a given design is from the optimal design. He characterized and constructed families of optimal designs. Some of the celebrated ones are balanced block designs, generalized Youden designs, and weighing designs. He also developed combinatorial structures of these designs."

Written by renowned experts in the field, this reissue of a textbook has as its unifying theme the role that probability models have had, and continue to have, in scientific and practical applications. It includes many examples, with actual data, of real-world use of probability models, while expositing the mathematical theory of probability at an introductory calculus-based level. Detailed descriptions of the properties and applications of probability models that have successfully modeled real phenomena are given, as well as an explanation of methods for testing goodness of fit of these models. Readers will receive a firm foundation in techniques for deriving distributions of various summaries of data that will prepare them for subsequent studies of statistics, as well as a solid grounding in concepts such as that of conditional probability that will prepare them for more advanced courses in stochastic processes.