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.

Although they play a fundamental role in nearly all branches of mathematics, inequalities are usually obtained by ad hoc methods rather than as consequences of some underlying "theory of inequalities." For certain kinds of inequalities, the notion of majorization leads to such a theory that is sometimes extremely useful and powerful for deriving inequalities. Moreover, the derivation of an inequality by methods of majorization is often very helpful both for providing a deeper understanding and for suggesting natural generalizations.Anyone wishing to employ majorization as a tool in applications can make use of the theorems; for the most part, their statements are easily understood.

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.

This book’s first edition has been widely cited by researchers in diverse fields. The following are excerpts from reviews. “Inequalities: Theory of Majorization and its Applications” merits strong praise. It is innovative, coherent, well written and, most importantly, a pleasure to read. … This work is a valuable resource!” (Mathematical Reviews). “The authors … present an extremely rich collection of inequalities in a remarkably coherent and unified approach. The book is a major work on inequalities, rich in content and original in organization.” (Siam Review). “The appearance of … Inequalities in 1979 had a great impact on the mathematical sciences. By showing how a single concept unified a staggering amount of material from widely diverse disciplines–probability, geometry, statistics, operations research, etc.–this work was a revelation to those of us who had been trying to make sense of his own corner of this material.” (Linear Algebra and its Applications). 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 reference list has almost doubled.

This book’s first edition has been widely cited by researchers in diverse fields. The following are excerpts from reviews. “Inequalities: Theory of Majorization and its Applications” merits strong praise. It is innovative, coherent, well written and, most importantly, a pleasure to read. … This work is a valuable resource!” (Mathematical Reviews). “The authors … present an extremely rich collection of inequalities in a remarkably coherent and unified approach. The book is a major work on inequalities, rich in content and original in organization.” (Siam Review). “The appearance of … Inequalities in 1979 had a great impact on the mathematical sciences. By showing how a single concept unified a staggering amount of material from widely diverse disciplines–probability, geometry, statistics, operations research, etc.–this work was a revelation to those of us who had been trying to make sense of his own corner of this material.” (Linear Algebra and its Applications). 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 reference list has almost doubled.

A Stanford University Press classic.

This SIAM Classics edition is an unabridged, corrected republication of the work first published in 1977. It provides a compendium of applied aspects of ordering and selection procedures and includes tables that permit the practitioner to carry out the experiment and draw statistically justified conclusions. These tables are not readily available in other texts. Although more than 1000 papers and several books on the general theory of ranking and selection have been published since this book first appeared, the methodology is presented in a more elementary fashion, with numerous examples to help the reader apply it to a specific problem.There is a dichotomy in modern statistics that distinguishes between analyses done before an experiment is completed and those done afterward. Ranking and selection methods are useful in both of these categories. The authors provide an alternative to the overused ""testing the null hypothesis"" when what the practitioner really needs is a method of ranking k given populations, selecting the t best populations, or some similar goal. That need and purpose is as important today as when the subject was first developed nearly 50 years ago.

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.

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

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.

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

Identification, Equivalent Models, and Computer Algebra provides information pertinent to computer algebra. This book presents a brief discussion of the commutation matrix, an operator that plays a role when derivatives have to be evaluated involving symmetric matrices. Organized into eight chapters, this book begins with an overview of the link between identification of a parameter and the existence of a consistent estimator, and the link between identification of a model and the rank of a Jacobian matrix. This text then describes an algorithm for the determination of the exact rank of a parametrized matrix. Other chapters consider the identification in the simultaneous equation model. This book discusses as well the identification assessment in confirmatory factor analysis, a problem related to the simultaneous equations model. The final chapter deals with various computer programs that the enclosed diskette contains. This book is a valuable resource for readers who are interested in computer algebra.

Regression Estimators: A Comparative Study presents, compares, and contrasts the development and the properties of the ridge type estimators that result from both Bayesian and non-Bayesian (frequentist) methods. The book is divided into four parts. The first part (Chapters I and II) discusses the need for alternatives to least square estimators, gives a historical survey of the literature and summarizes basic ideas in Matrix Theory and Statistical Decision Theory used throughout the book. The second part (Chapters III and IV) covers the estimators from both the Bayesian and from the frequentist points of view and explores the mathematical relationships between them. The third part (Chapters V-VIII) considers the efficiency of the estimators with and without averaging over a prior distribution. Part IV, the final two chapters IX and X, suggests applications of the methods and results of Chapters III-VII to Kaiman Filters and Analysis of Variance, two very important areas of application. Statisticians and workers in fields that use statistical methods who would like to know more about the analytical properties of ridge type estimators will find the book invaluable.

Statistical Modeling and Decision Science: Multi-Objective Programming in the USSR provides information pertinent to multi-objective programming that has emerged as an increasingly active area of research in the fields of applied mathematics, operations research, and decision and management science. This book traces and analyzes the development of Soviet multi-objective programming. Organized into 24 chapters, this book begins with an overview of the research institutes most actively involved in multi-objective programming research. This text then presents an analytical framework for grouping and classifying the diverse Soviet methods. Other chapters consider the methods and then evaluated according to the significance and soundness of its basic approach and its kinship to other methods. This book discusses as well some significant Soviet theoretical research and several distinctive approaches proposed by Soviet researchers for comparing the effectiveness of alternative interactive multi-objective programming method. The final chapter deals with distinctive Soviet tendencies in multi-objective research. This book is a valuable resource for economists.

Robustness of Statistical Tests provides a general, systematic finite sample theory of the robustness of tests and covers the application of this theory to some important testing problems commonly considered under normality. This eight-chapter text focuses on the robustness that is concerned with the exact robustness in which the distributional or optimal property that a test carries under a normal distribution holds exactly under a nonnormal distribution. Chapter 1 reviews the elliptically symmetric distributions and their properties, while Chapter 2 describes the representation theorem for the probability ration of a maximal invariant. Chapter 3 explores the basic concepts of three aspects of the robustness of tests, namely, null, nonnull, and optimality, as well as a theory providing methods to establish them. Chapter 4 discusses the applications of the general theory with the study of the robustness of the familiar Student''s r-test and tests for serial correlation. This chapter also deals with robustness without invariance. Chapter 5 looks into the most useful and widely applied problems in multivariate testing, including the GMANOVA (General Multivariate Analysis of Variance). Chapters 6 and 7 tackle the robust tests for covariance structures, such as sphericity and independence and provide a detailed description of univariate and multivariate outlier problems. Chapter 8 presents some new robustness results, which deal with inference in two population problems. This book will prove useful to advance graduate mathematical statistics students.

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 book’s first edition has been widely cited by researchers in diverse fields. The following are excerpts from reviews. “Inequalities: Theory of Majorization and its Applications” merits strong praise. It is innovative, coherent, well written and, most importantly, a pleasure to read. … This work is a valuable resource!” (Mathematical Reviews). “The authors … present an extremely rich collection of inequalities in a remarkably coherent and unified approach. The book is a major work on inequalities, rich in content and original in organization.” (Siam Review). “The appearance of … Inequalities in 1979 had a great impact on the mathematical sciences. By showing how a single concept unified a staggering amount of material from widely diverse disciplines–probability, geometry, statistics, operations research, etc.–this work was a revelation to those of us who had been trying to make sense of his own corner of this material.” (Linear Algebra and its Applications). 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 reference list has almost doubled.

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.