Ingram Olkin

Theodore Wilbur Anderson

Multivariate Analysis and Its Applications

Lyle V. Jones

Evolution and Perspectives

The Nation's Report Card

Cyrus Derman

Leon Jay Gleser

A Guide to Probability Theory and Application

Yung Liang Tong

Proceedings of the Symposium on Inequalities in Statistics and Probability, October 27-30, 1982, Lincoln, Nebraska

Inequalities in Statistics and Probability

Barry C. Arnold

Albert W. Marshall

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.

Inequalities: Theory of Majorization and Its Applications

Leon J Gleser

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.

Probability Models And Applications (Revised Second Edition)

Larry V. Hedges

Professor Larry V Hedges

Preface. -- Introduction. -- Data Sets. -- Tests of Statistical Significance of Combined Results. -- Vote-Counting Methods. -- Estimation of a Single Effect Size: Parametric and Nonparametric Methods. -- Parametric Estimation of Effect Size from a Series of Experiments. -- Fitting Parametric Fixed Effect Models to Effect Sizes: Categorical Methods. -- Fitting Parametric Fixed Effect Models to Effect Sizes: General Linear Models. -- Random Effects Models for Effect Sizes. -- Multivariate Models for Effect Sizes. -- Combining Estimates of Correlation Coefficients. -- Diagnostic Procedures for Research Synthesis Models. -- Clustering Estimates of Effect Magnitude. -- Estimation of Effect Size When Not All Study Outcomes Are Observed. -- Meta-Analysis in the Physical and Biological Sciences. -- Appendix. -- References. -- Index.

Ingram Olkin

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