Econometrics and economic statistics Books

Book SynopsisMany of the complex problems faced by decision makers involve uncertainty as well as multiple conflicting objectives. This book provides a complete understanding of the types of objective functions that should be used in multiattribute decision making. By using tools such as preference, value, and utility functions, readers will learn state-of-the-art methods to analyze prospects to guide decision making and will develop a process that guarantees a defensible analysis to rationalize choices. Summarizing and distilling classical techniques and providing extensive coverage of recent advances in the field, the author offers practical guidance on how to make good decisions in the face of uncertainty. This text will appeal to graduate students and practitioners alike in systems engineering, operations research, business, management, government, climate change, energy, and healthcare.Trade Review'Ali E. Abbas' new book on utility theory is a comprehensive treatment ranging from simple distinctions about facing uncertainty to general multiattribute utility theory. Beginning with a general approach to MAU, it then specializes to show how popular versions, such as additive utility, are actually very restrictive and other simple approaches, such as additive value functions with risk aversion, are much more general. He derives the complete forms of multiattribute utility functions having additive value functions. With utility transversality, he also demonstrates that there is only one dimension of risk attitude, as the value function translates risk attitude across dimensions. Abbas's book promises to be the standard reference work for decades to come.' Jim Matheson, Chairman and CFO, SmartOrg, Inc.'Foundations of Multiattribute Utility provides a much-needed update, a comprehensive structure, and indeed a vision for a field that has blossomed in the forty years since Keeney and Raiffa's Decisions with Multiple Objectives. The book synthesizes recent results by others and by the author, including an elegant copula representation. Thanks to the book's accessible style - which even includes Socratic dialogues - readers will appreciate its place within the rich mathematics of preference and economic utility theory and the rich practice of decision analysis. The book offers many rewards for modelers in these fields, while for researchers it is required reading.' Jeffrey M. Keisler, University of Massachusetts, Boston'The definitive work on preference, value, and utility that can serve the full range of decision situations. If you need to tackle a complex multiattribute preference problem, the answer is in this book. Ali E. Abbas has created a great text of a graduate course and a valuable reference for the practitioner.' Carl Spetzler, Strategic Decisions Group and author of Decision Quality'In Foundations of Multiattribute Utility, Ali E. Abbas has brought together divergent themes of research in decision-making with multiple objectives into a single, coherent whole. This book can serve as an introduction for decision-makers who want to think carefully about making trade-offs. It is also a reference for researchers who want to understand the latest developments in the field. I know I will be keeping this book close at hand in my office for years to come.' Jason Merrick, Virginia Commonwealth University'This book is a long overdue. It is encyclopedic in nature, chronicling many recent developments in utility theory, while summarizing the classical results in the field. The book is a perfect companion to Keeney and Raiffa's (1976) seminal book on multiattribute utility. It should be on the desk of any decision analyst, economist, or statistician who does research in or teaches utility theory.' Kenneth C. Lichtendahl, Jr, University of VirginiaTable of ContentsPart I. Foundations of Preference, Value, and Utility: 1. Preference, value, and utility; 2. Foundations of expected utility; 3. Some flawed methods of decision making; Part II. Deterministic Decisions: 4. The order rule: preferences for deterministic consequences; 5. Getting the attributes right; 6. Preference functions for single attributes; 7. Preference functions for multiple attributes; 8. Additive preference functions; 9. Valuing deterministic consequences – value functions; Part III. Decisions with Uncertainty: Multi-attribute Utility Functions Using a Value Measure: 10. Single-attribute utility function; 11. Multi-attribute utility functions using preference and value functions; 12. Utility functions over additive preference or value functions; Part IV. Decisions with Uncertainty: Properties of Single-Attribute Utility Functions: 13. The role of the utility function in valuing uncertain deals; 14. The risk aversion function; 15. Scale transformations applied to lottery outcomes; 16. The invariant transformation of a utility function; 17. Valuing changes in investment opportunities; 18. One-switch utility functions; 19. Utility transversality; 20. Multi-attribute risk aversion; Part V. Decisions with Uncertainty: Multi-attribute Utility Functions without Preference or Value Functions: 21. Attribute dominance utility; 22. The two-attribute utility tree; 23. Independence conditions for two attributes; 24. The multi-attribute utility tree; 25. Independence conditions for three or more attributes; 26. Higher-order independence conditions; 27. One-switch utility independence; Part VI. Utility Copula Formulations: 28. Multi-attribute utility copulas; 29. Archimedean utility copulas; 30. Assessing Archimedean utility copulas; 31. Utility copula functions matching all boundary assessments; 32. Bidirectional one-switch ordinal copulas; 33. Reflections of what we have learned.

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Book SynopsisDesigned for the intellectually curious, this book provides a solid foundation in basic probability theory in a charming style, without technical jargon. This text will immerse the reader in a mathematical view of the world, and teach them techniques to solve real-world problems both inside and outside the casino.Trade Review'Probability is a subject of fundamental importance that's often taught as a dry slog through ball-filled urns. Fat Chance, a snappy and example-rich text, is the perfect antidote and a great choice for a general-audience math course.' Jordan Ellenberg, University of Wisconsin-Madison'Mathematics is a minority sport. Only very few understand, say, algebraic geometry or ergodic theory, and that's perfectly OK. Yet, probability theory is different. We are surrounded by chance and uncertainty. You cannot be a fully-fledged, functioning twenty-first-century human, whether at work, as a consumer or as an active citizen, without understanding the basic rules that govern chance. This is a book explaining chance and probability to non-mathematicians: accessible, not expecting any prior knowledge - but, there being no free lunch, assuming basic intelligence and an open mind. A small price for great enlightenment.' Arieh Iserles, University of Cambridge'Fat Chance is a fun and friendly introduction to the big ideas of risk, probability, and uncertainty in our everyday lives and in the world around us. It's written by three of the greatest mathematicians - and teachers - anywhere on the planet. I loved it! And according to my calculations, there's a high probability you'll love it too.' Steven Strogatz, Cornell University, and author of Infinite Powers'… Fat Chance is an enjoyable small introductory book that, without the pretension of serving as a reference textbook, will certainly help undergraduate students to approach the fascinating world of probability but will also be appreciated by whoever desires to learn the basics through self-education.' Massimo Nespolo, Journal of Applied Crystallography'The book is an excellent text for anyone looking for a very enjoyable introduction to probability. It can be read on its own or used as a textbook for a math course for non-math majors.' Thomas Hagedorn, MAA ReviewsTable of ContentsPart I. Counting: 1. Simple counting; 2. The multiplication principle; 3. The subtraction principle; 4. Collections; 5. Games of chance; Interlude; 6. The binomial theorem; 7. Advanced counting; Part II. Probability: 8. Expected value; 9. Conditional probability; 10. Unfair coins and loaded dice; 11. Geometric probability; Part III. Probability in the Large: 12. Games and their payoffs; 13. The normal distribution; 14. Don't try this at home.

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Book SynopsisAt the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.Trade ReviewFrom the reviews: "This is certainly a very valuable book for anyone interested in this new methodology." N.D.C. Veraverbeke for Short Book Reviews of the ISI, December 2006 "The present book does bring something new and, indeed some novel theoretical investigations into the kinds of functional data problems … . I do think the present book is a worthy contribution to the literature. The authors have done a nice job of summarizing some of ongoing research … . Researchers in the growing functional statistics community should be glad to have a copy of the book." (Z. Q. John Lu, Technometrics, Vol. 49 (2), 2007) "This book presents new nonparametric staustical methods for samples of functional data … . The computational aspects of the book are oriented toward practitioners whereas open problems emerging from this new field of statistics will attract Ph. D. students and academic researchers. This book is also accessible to graduate students starting out in the area of functional statistics." (Fazil A. Aliev, Mathematical Reviews, Issue 2007 b) "Nonparametric Functional Data Analysis explores nonparametric methods as that can be applied to functional data, developing new methods and providing theoretical results for the conditional and unconditional mean, median, and mode for independent and dependent functional data. … As a resource for those interested in FDA research and methods, it is highly recommended. … This book should spur new and exciting research in FDA, and it provides new tools that are ready for application to real data sets." (Mark Greenwood, Journal of the American Statistical Association, Vol. 102 (479), 2007) "Example data sets that motivate the development of the models are also provided. … The index provided seems to be fairly complete and is helpful in looking up topics discusses in this monograph. Several chapters end in a section in which the authors provide additional comments, discussions and pose some open problems in this area, which should be appealing for researchers in this field. … This book should be useful for all people interested in the area of functional data analysis." (Anatolij Dvurecenskij, Zentralblatt MATH, Vol. 1119 (21), 2007)Table of ContentsIntroduction to functional nonparametric statistics.- Some functional datasets and associated statistical problematics.- What is a well adapted space for functional data?.- Local weighting of functional variables.- Functional nonparametric prediction methodologies.- Some selected asymptotics.- Computational issues.- Nonparametric supervised classification for functional data.- Nonparametric unsupervised classification for functional data.- Mixing, nonparametric and functional statistics.- Some selected asymptotics.- Application to continuous time processes prediction.- Small ball probabilities, semi-metric spaces and nonparametric statistics.- Conclusion and perspectives.

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Book SynopsisTo facilitate the use of R, the authors have developed an R package called Benchmarking, which implements the main methods within both DEA and SFA.The book uses mathematical formulations of models and assumptions, but it de-emphasizes the formal proofs - in part by placing them in appendices -- or by referring to the original sources.Trade ReviewFrom the reviews:“Benchmarking with DEA, SFA, and R covers data envelopment analysis (DEA) and stochastic frontier analysis (SFA) … . Although more advanced readers … will gain more from reading this book, it contains something for all who are interested in the principles underlying DEA and SFA and who want to know how to use these methods … . the book is appropriate for readers who have a fairly good foundation in the methods of DEA and SFA and wish to raise their level of understanding.” (Emmanuel Thanassoulis, Interfaces, Vol. 42 (3), May-June, 2012)Table of Contents1. Introduction to Benchmarking.- 2. Efficiency Measures.- 3. Production Models and Technology.- 4. Data Envelopment Analysis DEA.- 5. Additional Topics in DEA.- 6. Statistical Analysis in DEA.- 7. Stochastic Frontier Analysis SFA.- 8. Additional Topics in SFA.- 9. Merger Analysis.- 10. Regulation and Contracting.- Appendix. Getting Started with R: A Quick Introduction.

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