This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

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Book Description
Human existence is based upon risk. This text charts the adventures of a group of thinkers who embarked on a voyage of intellectual discovery, transforming primeval superstition into the powerful tools of risk control employed today.
Amazon.com
With the stock market breaking records almost daily, leaving longtime market analysts shaking their heads and revising their forecasts, a study of the concept of risk seems quite timely. Peter Bernstein has written a comprehensive history of man's efforts to understand risk and probability, beginning with early gamblers in ancient Greece, continuing through the 17th-century French mathematicians Pascal and Fermat and up to modern chaos theory. Along the way he demonstrates that understanding risk underlies everything from game theory to bridge-building to winemaking.
From Publishers Weekly
Risk management, which assumes that future risks can be understood, measured and to some extent predicted, is the focus of this solid, thoroughgoing history. Probability theory, pioneered by 17th-century French mathematicians Blaise Pascal and Pierre de Fermat, has made possible the design of great bridges, electric power utilities and insurance policies. The statistical sampling methods invented by dour Swiss scientist Jacob Bernoulli undergird diverse activities such as the testing of new drugs, stock-picking and wine tasting. Bernstein (Capital Ideas) animates his narrative with a colorful cast of risk-analyzers, including gambling addict Girolamo Cardano, 16th-century Italian physician to the Pope; and John Maynard Keynes, whose concerns over economic uncertainty compelled him to recommend an active, interventionist role for government. Bernstein also traces the development of business forecasting, game theory, insurance and derivatives, and surveys recent advances in risk forecasting made possible through chaos theory and by the development of neural networks.
From Library Journal
For several centuries, mathematics has been the language of the exact sciences. Only in the 20th century has mathematics become predominant in other fields, particularly economics and finance. In this book, Bernstein (Capital Ideas: The Improbable Origins of Modern Wall Street, LJ 12/91), head of an economic consulting firm, traces the development of probability theory from its beginnings in analyzing games of chance, through its application to statistical theory and insurance, up to its present use in developing investment strategies to control risk. He includes excellent sections on portfolio analysis and on investments in derivatives. Bernstein clearly describes the people, their work, and the events that have revolutionized the thinking on Wall Street. A worthwhile acquisition for business and math collections.
Harold D. Shane, Baruch Coll., CUNY
From Booklist
Bernstein's lively history chronicles a profound transformation in attitudes about the future. How one's fate changed from depending less on capricious outcomes and more on predictable ones forms the backbone of the narrative. His central characters are mathematicians who began pondering the statistics of gambling, or gamblers pondering the risks of gambling: about one sixteenth-century polymath, Girolamo Cardano, Bernstein writes that his "credentials as a gambling addict alone would justify his appearance in the history of risk," and that comment is typical of Bernstein's engaging presentation. Amid his recounting of the insights into probability from Pascal to Keynes, he touches on an array of modern fields in which risk analysis is crucial--insurance, commodities futures, stock markets, and that old standard, gambling. This cornucopia of biographical sketches, mathematical examples, and reflections on the nature of human expectations about the future faces little risk of idling in libraries; patrons of the business section might be keenest to read it.
Gilbert Taylor
From AudioFile
Jesse Boggs honed his expressive, laid-back vocal style narrating his own award-winning documentaries. Here, as reader and abridger, he goes a long way to clarify Bernstein's convoluted theory of risk management. His careful phrasing also brings into high relief the sweeping generalizations and questionable axioms that give pause to the analytic listener. Only in this careful frame of mind can one separate wheat from chaff and learn whatever this book has to teach. Y.R.
The Washington Post Book World, September 20, 1998
AGAINST THE GODS appeared in the "Washington Is Also Reading..." section of The Washington Post Book World. The book is described as, "A comprehensive history of man's efforts to understand risk and probability, from ancient gamblers in Greece to modern chaos theory."
Book Dimension
length: (cm)22.7 　　　 　　　　　 　　　　　 width:(cm)15.2

That at London the hospitals are better and more desirable than those of Paris, for that in the best at Paris there die two out of fifteen, whereas at London there die out of the worst scarce 2 out of 16, and yet but a fiftieth part of the whole die out of the hospitals at London, and two-fifths, or twenty times that proportion die out of the Paris hospitals which are of the same kind; that is to say, the number of those at London, who choose to lie sick in hospitals rather than in their own houses, are to the like people of Paris as one to twenty; which shows the greater poverty or want of means in the people of Paris than those of London.

Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views.

Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.
Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!
Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.
Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton

《概率论沉思录(英文版)》将概率和统计推断融合在一起，用新的观点生动地描述了概率论在物理学、数学、经济学、化学和生物学等领域中的广泛应用，尤其是它阐述了贝叶斯理论的丰富应用，弥补了其他概率和统计教材的不足。全书分为两大部分。第一部分包括10章内容，讲解抽样理论、假设检验、参数估计等概率论的原理及其初等应用；第二部分包括12章内容，讲解概率论的高级应用，如在物理测量、通信理论中的应用。《概率论沉思录(英文版)》还附有大量习题，内容全面，体例完整。
《概率论沉思录(英文版)》内容不局限于某一特定领域，适合涉及数据分析的各领域工作者阅读，也可作为高年级本科生和研究生相关课程的教材。

Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.

First Edition, Winner of the prestigious William James Award from the American Psychological Association An understanding of the principles of rational decision making can help students improve the quality of their lives. Intended as an introductory textbook, the material in Rational Choice in an Uncertain World is not only of scholarly interest, but practical as well. Created specifically for courses on judgement and decision-making, this book makes research readily accessible to both undergraduate and graduate students. This Second Edition of the award-winning book, Rational Choice in an Uncertain World (1988) by Robyn M. Dawes, is sure to interest and enlighten students at all levels. This new edition features: * New student friendly chapter introductions as well as conclusions and cross-references between chapters. * Award-winning authors are respected professors with over 30 years of experience in the field. * Practical, everyday examples from such areas as finance, medicine, law, and engineering.* Comprehensive and up-to-date information keep this edition abreast of the changing ideas within the discipline * Additional discussion of the descriptive, psychological models of decision making to expand upon the original emphasis on normative, rational, 'Expected Utility Theory' models. Equipped with this knowledge and an understanding of the principles of rational decision making, both undergraduate and graduate students can help improve the quality of their choices and, thus, their life.