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D. R. Anderson, Colorado State University, Fort J. Franke, W. Härdle, C. M. Hafner U. B. Kjaerulff, Aalborg University, Aalborg, Denmark; Collins, CO, USA A. L. Madsen, HUGIN Expert A/S, Aalborg, Denmark Statistics of Financial Markets Model Based Inference in the An Introduction Bayesian Networks and Life Sciences Influence Diagrams: A Guide A Primer on Evidence to Construction and Analysis Statistics of Financial Markets offers a vivid yet concise introduction to the growing field of statis- The abstract concept of “information” can be tical applications in finance. The reader will learn Probabilistic networks, also known as Bayesian quantified and this has led to many important the basic methods to evaluate option contracts, to networks and influence diagrams, have become advances in the analysis of data in the empirical analyse financial time series, to select portfolios one of the most promising technologies in the area sciences. This text focuses on a science philosophy and manage risks making realistic assumptions of of applied artificial intelligence, offering intui- based on “multiple working hypotheses” and statis- the market behaviour. tive, efficient, and reliable methods for diagnosis, tical models to represent them. The fundamental The focus is both on fundamentals of math- prediction, decision making, classification, trou- science question relates to the empirical evidence ematical finance and financial time series analysis bleshooting, and data mining under uncertainty. for hypotheses in this set - a formal strength of and on applications to given problems of financial Bayesian Networks and Influence Diagrams: A evidence. Kullback-Leibler information is the markets, making the book the ideal basis for Guide to Construction and Analysis provides a information lost when a model is used to approxi- lectures, seminars and crash courses on the topic. comprehensive guide for practitioners who wish mate full reality. Hirotugu Akaike found a link For the second edition the book has been updated to understand, construct, and analyze intelligent between K-L information (a cornerstone of infor- and extensively revised. Several new aspects have systems for decision support based on probabilistic mation theory) and the maximized log-likelihood been included, among others a chapter on credit networks. Intended primarily for practitioners, this (a cornerstone of mathematical statistics). This risk management. book does not require sophisticated mathematical combination has become the basis for a new para- skills or deep understanding of the underlying digm in model based inference. The text advocates Features theory and methods nor does it discuss alterna- formal inference from all the hypotheses/models in 7 Ideal basis for lectures, seminars, and crash tive technologies for reasoning under uncertainty. the a priori set - multimodel inference. courses on statistical applications in finance The theory and methods presented are illustrated 7 Interactive approach using statistical software through more than 140 examples, and exercises Features are included for the reader to check his/her level of 7 Very broad applicability, very science-based, Contents understanding. and practical 7 Very powerful – the concept Option Pricing.- Statistical Model of Financial of formal “strength of evidence” 7 Simple to Time Series.- Selected Financial Applications. Features use and understand 7 Emphasis on science 7 Comprehensive introduction to probabilistic philosophy, not just “data analysis” Fields of interest networks 7 Written specifically for practitioners Statistics for Business/Economics/Mathematical of applied artificial intelligence 7 Complete guide Contents Finance/Insurance; Quantitative Finance; Finance / to understand, construct, and analyze probabilistic Introduction-science hypotheses and science Banking networks philosophy.- Data and models.- Information theory and entropy.- Quantifying the evidence Target groups Contents about science hypotheses.- Multimodel inference.- Students in financial engineering, statistics, math- Introduction.- Networks.- Probabilities.- Probabi- Advanced topics.- Summary. ematical finance, and financial econometrics listic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data- Fields of interest Type of publication Driven Modeling.- Conflict Analysis.- Sensitivity Statistics for Life Sciences, Medicine, Health Graduate/Advanced undergraduate textbook Analysis.- Value of Information Analysis. Sciences; Environmental Monitoring/Analysis; Ecology Fields of interest Statistics and Computing/Statistics Programs; Target groups Probability and Statistics in Computer Science Graduate students, scientists Target groups Type of publication Practitioners Graduate/Advanced undergraduate textbook Type of publication Monograph
Due January 2008 Due December 2007 Due January 2008 2008. Approx. 345 p. (Information Science and Statistics) 2008. Approx. 190 p. 8 illus. Softcover 2nd ed. 2008. XXII, 495 p. (Universitext) Softcover Hardcover 7 approx. € 30,80 | £23.50 7 approx. € 69,95 | £54.00 7 approx. € 61,60 | £47.50 9
S. Konishi, Kyushu University, Fukuoka, Japan; F. Liese, Universität Rostock, Germany; K. Miescke, J. Shao, University of Wisconsin, Madison, WI, USA G. Kitagawa, The Institute of Statistical Mathematics, University of Illinois at Chicago, IL, USA Tokyo, Japan Mathematical Statistics Statistical Decision Theory Information Criteria and Estimation, Testing, and Selection Statistical Modeling This graduate textbook covers topics in statistical theory essential for graduate students preparing This monograph is written for advanced graduate for work on a Ph.D. degree in statistics. The first The Akaike information criterion (AIC) derived as students, Ph.D. students, and researchers in math- chapter provides a quick overview of concepts an estimator of the Kullback-Leibler information ematical statistics and decision theory. All major and results in measure-theoretic probability discrepancy provides a useful tool for evaluating topics are introduced on a fairly elementary level theory that are useful in statistics. The second statistical models, and numerous successful and then developed gradually to higher levels. The chapter introduces some fundamental concepts in applications of the AIC have been reported in book is self-contained as it provides full proofs, statistical decision theory and inference. Chapters various fields of natural sciences, social sciences worked-out examples, and problems. It can be used 3-7 contain detailed studies on some important and engineering. as a basis for graduate courses, seminars, Ph.D. topics: unbiased estimation, parametric estimation, One of the main objectives of this book is to programs, self-studies, and as a reference book. nonparametric estimation, hypothesis testing, and provide comprehensive explanations of the The authors present a rigorous account of the confidence sets. A large number of exercises in concepts and derivations of the AIC and related concepts and a broad treatment of the major each chapter provide not only practice problems criteria, including Schwarz’s Bayesian informa- results of classical finite sample size decision for students, but also many additional results. tion criterion (BIC), together with a wide range of theory and modern asymptotic decision theory. practical examples of model selection and evalu- Highlights are systematic applications to the fields Contents ation criteria. A secondary objective is to provide of parameter estimation, testing hypotheses, and Probability Theory.- Fundamentals of Statistics.- a theoretical basis for the analysis and extension selection of populations. Unbiased Estimation.- Estimation in Parametric of information criteria via a statistical functional Models.- Estimation in Nonparametric Models.- approach. Features Hypothesis Tests.- Confidence Sets. 7 Presents the main ideas of decision theory in an Features organized, balanced, and mathematically rigorous Field of interest 7 With the development of modeling techniques, manner, while observing statistical relevance Statistical Theory and Methods it has been required to construct model selection criteria, relaxing the assumptions imposed AIC Contents Target groups and BIC Statistical models.- Tests in models with mono- Graduate students tonicity properties.- Statistical decision theory.- Contents Comparison of models, reduction by sufficiency.- Type of publication Concept of statistical modeling.- Statistical Invariant statistical decision models.- Large sample Graduate/Advanced undergraduate textbook models.- Information criterion.- Statistical approximations of models and decisions.- Estima- modeling by AIC.- Generalized information crite- tion.- Testing.- Selection. rion GIC.- Statistical modeling by GIC.- Theoret- ical development and asymptotic properties of the Fields of interest GIC.- Bootstrap information criterion.- Bayesian Statistical Theory and Methods information criteria.- Various model evaluation criteria. Target groups Graduate students, researchers Fields of interest Statistical Theory and Methods; ; Probability and Type of publication Statistics in Computer Science Monograph
Target groups Researchers
Type of publication Monograph
Due December 2007 Available Due November 2007 2008. Approx. 695 p. (Springer Series in Statistics) 2nd ed. 2003. Corr. 4th printing 2008. XVI, 591 p. 2008. XII, 276 p. (Springer Series in Statistics) Hardcover Hardcover (Springer Texts in Statistics) Hardcover 7 € 64,95 | £50.00 7 approx. € 69,95 | £54.00 7 € 79,95 | £61.50 9