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Measuring Risk in Complex Stochastic Systems



Measuring Risk in Complex Stochastic Systems
Publisher: Springer | ISBN: 038798996X | edition 2000 | PDF | 251 pages | 11,28 mb

During the last decade, problems in the world of finance have been the main driving force for developing sophisticated mathematical methods which may be used for identifying and measuring risk. The focus is still on quantifying market and credit risk, but general operational risks will become more important in the future. In this book the reader will find approaches from economic theory, allocation problems, credit scoring, volatility structures, general market risk, country risk and extreme value theory. The contributions of this book reflect the views of leading practitioners and academics in the field of risk management. Most of the models considered for the evolution of asset values are of a complex and stochastic nature, including stochastic volatility models in continuous time as well as their counterparts in discrete time, the family of GARCH-like time series. The contents reflect the fact that a major part of recent research has been motivated by applications in finance, but most of the mathematical approaches may be used for risk analysis in engineering and science in a rather straightforward manner. As known from insurance mathematics for some time, extreme damages from natural disaster follow similar stochastic laws as extreme losses from certain investments.




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Model-Oriented Design of Experiments

Topics in Optimal Design
مع خالص تقديري لاعمالكم الخيرة المقدمة في موقعك الرائع وجازاكم الله كل الخير على كل مساعدة

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Statistical Analysis Quick Reference Guidebook: With SPSS Examples



Sage Publications, Inc, "Statistical Analysis Quick Reference Guidebook: With SPSS Examples"
Sage Publications, Inc | 2006 | ISBN: 1412925606 | 280 pages | PDF | 2,3 MB

Statistical Analysis Quick Reference Guidebook: With SPSS Examples is a practical "cut to the chase" handbook that quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision-making in a wide variety of disciplines. In this one-stop reference, authors Alan C. Elliott and Wayne A. Woodward provide succinct guidelines for performing an analysis, avoiding pitfalls, interpreting results, and reporting outcomes.

Summary: Best SPSS resource I've seen!
Rating: 5

I just received my copy of this book, and it looks great! If I were teaching a research methods class, I'd adopt this as a required text. In fact, I used the book today when one of my college students asked how to report the results of a test in an academic paper. I quickly turned to the right section and consulted the "How to report..." section. Excellent!


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Probability and Induction




Matthew Kneale, "Probability and Induction"
Oxford at the Clarendon Press | 1949 | ISBN-10: N/A | ASIN: B0014O6P14 | 264 pages | Djvu | 3,5 MB




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Linear model theory: univariate, multivariate, and mixed models
By Keith E. Muller, Paul Wilder Stewart
ولكم جزيل الشكر واضحى مبارك على الجميع

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Mathematical Statistics (Springer Series in Statistics)




Mathematical Statistics (Springer Series in Statistics)
Publisher: Springer | ISBN: 038798674X | edition 1999 | PDF | 545 pages | 31,05 mb

This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statistics. The second chapter introduces some fundamental concepts in statistical decision theory and inference. Chapters 3-7 contain detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. A large number of exercises in each chapter provide not only practice problems for students, but also many additional results. In addition to the classical results that are typically covered in a textbook of a similar level, this book introduces some topics in modern statistical theory that have been developed in recent years, such as Markov chain Monte Carlo, quasi-likelihoods, empirical likelihoods, statistical functionals, generalized estimation equations, the jackknife, and the bootstrap. Jun Shao is Professor of Statistics at the University of Wisconsin, Madison.




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Smoothing of Multivariate Data: Density Estimation and Visualization (Wiley Series in Probability and Statistics)




Jussi Klemelä "Smoothing of Multivariate Data: Density Estimation and Visualization (Wiley Series in Probability and Statistics)"
Wiley | English | 2009-08-17 | ISBN: 0470290889 | 603 pages | PDF | 10 MB


An applied treatment of the key methods and state-of-the-art tools for visualizing and understanding statistical data

Smoothing of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use of visualization tools. Rather than outlining the theoretical concepts of classification and regression, this book focuses on the procedures for estimating a multivariate distribution via smoothing.

The author first provides an introduction to various visualization tools that can be used to construct representations of multivariate functions, sets, data, and scales of multivariate density estimates. Next, readers are presented with an extensive review of the basic mathematical tools that are needed to asymptotically analyze the behavior of multivariate density estimators, with coverage of density classes, lower bounds, empirical processes, and manipulation of density estimates. The book concludes with an extensive toolbox of multivariate density estimators, including anisotropic kernel estimators, minimization estimators, multivariate adaptive histograms, and wavelet estimators.

A completely interactive experience is encouraged, as all examples and figurescan be easily replicated using the R software package, and every chapter concludes with numerous exercises that allow readers to test their understanding of the presented techniques. The R software is freely available on the book's related Web site along with "Code" sections for each chapter that provide short instructions for working in the R environment.

Combining mathematical analysis with practical implementations, Smoothing of Multivariate Data is an excellent book for courses in multivariate analysis, data analysis, and nonparametric statistics at the upper-undergraduate and graduatelevels. It also serves as a valuable reference for practitioners and researchers in the fields of statistics, computer science, economics, and engineering.

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Sampling Statistics (Wiley Series in Survey Methodology)




Wayne A. Fuller "Sampling Statistics (Wiley Series in Survey Methodology)"
Wiley | English | 2009-08-03 | ISBN: 0470454601 | 454 pages | PDF | 5 MB


Discover the latest developments and current practices in survey sampling

Survey sampling is an important component of research in many fields, and as the importance of survey sampling continues to grow, sophisticated sampling techniques that are both economical and scientifically reliable are essential to planning statistical research and the design of experiments. Sampling Statistics presents estimation techniques and sampling concepts to facilitate the application of model-based procedures to survey samples.

The book begins with an introduction to standard probability sampling concepts, which provides the foundation for studying samples selected from a finite population. The development of the theory of complex sampling methods is detailed, and subsequent chapters explore the construction of estimators, sample design, replication variance estimation, and procedures such as nonresponse adjustment and small area estimation where models play a key role. A final chapter covers analytic studies in which survey data are used for the estimation of parameters for a subject matter model.

The author draws upon his extensive experience with survey samples in the book's numerous examples. Both the production of "general use" databases and the analytic study of a limited number of characteristics are discussed. Exercises at the end of each chapter allow readers to test their comprehension of the presented concepts and techniques, and the references provide further resources for study.

Sampling Statistics is an ideal book for courses in survey sampling at the graduate level. It is also a valuable reference for practicing statisticians who analyze survey data or are involved in the design of sample surveys.


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Resampling Methods: A Practical Guide to Data Analysis




Resampling Methods: A Practical Guide to Data Analysis
Birkhäuser Boston | September 8, 2005 | ISBN: 0817643869 | 218 pages | PDF | 5 MB

This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, the book provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.

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Statistics for Business and Economics (4th Edition)




Paul Newbold, "Statistics for Business and Economics (4th Edition)"
Prentice Hall | 1994 | ISBN: 013188090X, 0131815954 | 867 pages | PDF | 36,2 MB




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Statistical Models: Theory and Practice




David A. Freedman, "Statistical Models: Theory and Practice"
Cambridge University Press | 2009 | ISBN: 0521112435, 0521743850 | 456 pages | PDF | 1,5 MB

This lively and engaging textbook explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The author, David A. Freedman, explains the basic ideas of association and regression, and takes you through the current models that link these ideas to causality. The focus is on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs with sample computer programs. The book is rich in exercises, most with answers. Target audiences include advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modeling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. Features of the book: • authoritative guidance from a well-known author with wide experience in teaching, research, and consulting • careful analysis of statistical issues in substantive applications • no-nonsense, direct style • versatile structure, enabling the text to be used as a text in a course, or read on its own • text that has been thoroughly class-tested at Berkeley • background material on regression and matrix algebra • plenty of exercises, most with solutions • extra material for instructors, including data sets and code for lab projects (available from Cambridge University Press) • many new exercises and examples • reorganized, restructured, and revised chapters to aid teaching and understanding



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Statistical Software Engineering




National Research Council, «Statistical Software Engineering»
National Academy Press | ISBN: 0309053447 | 1996 | PDF | 84 pages | 4.28 MB

This book identifies challenges and opportunities in the development and implementation of software that contain significant statistical content. While emphasizing the relevance of using rigorous statistical and probabilistic techniques in software engineering contexts, it presents opportunities for further research in the statistical sciences and their applications to software engineering. It is intended to motivate and attract new researchers from statistics and the mathematical sciences to attack relevant and pressing problems in the software engineering setting. It describes the "big picture," as this approach provides the context in which statistical methods must be developed. The book's survey nature is directed at the mathematical sciences audience, but software engineers should also find the statistical emphasis refreshing and stimulating. It is hoped that the book will have the effect of seeding the field of statistical software engineering by its indication of opportunities where statistical thinking can help to increase understanding, productivity, and quality of software and software production.

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Precedence-Type Tests and Applications



Precedence-Type Tests and Applications
324 pages | Wiley-Interscience; 1 edition (April 21, 2006) | 0471457205 | PDF | 11 Mb

Full coverage of statistical techniques for developing and implementing precedence-type tests

Precedence-Type Tests and Applications provides a comprehensive overview of theoretical and applied approaches to a variety of problems in which precedence-type test procedures can be used. The authors clearly demonstrate the effectiveness of these tests in life-testing situations designed for making quick and reliable decisions in the early stages of an experiment. Most of the text's examples use life-time data; however, theoretical properties are also discussed in the context of precedence testing. Monte Carlo studies are used to illustrate important results.

Following the authors' careful step-by-step instructions and guidance, readers master the wide range of statistical techniques involved in the development and implementation of precedence-type tests. The book covers the foundations of precedence testing research from the early 1960s up to the most recent theory and applications, including the authors' current contributions to the field. The book features the following parts:
* Part A deals with the original precedence test and some properties of precedence and related test procedures
* Part B explores alternatives to precedence testing, including maximal precedence, weighted forms of precedence and maximal precedence, and Wilcoxon-type rank-sum precedence tests and their properties
* Part C compares the extension of precedence, maximal precedence, and Wilcoxon-type rank-sum precedence tests to situations in which the sample arising from the life-testing experiment is progressively Type-II censored
* Part D examines precedence-type tests in multi-sample situations and selection problems

Tables are presented throughout the book to facilitate the application of the tests to practical problems. Helpful examples illustrate all of the precedence-type procedures, and an extensive bibliography enables readers to explore specialized topics in greater depth.

This book is a recommended reference for researchers and practitioners in reliability and life-time data analysis, applied probabilists, and engineers. It also serves as a supplemental text for courses in nonparametric statistics and reliability.

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Statistics in Spectroscopy by Howard Mark, Jerry Workman Jr




Statistics in Spectroscopy by Howard Mark, Jerry Workman Jr. (Repost)
Publisher: Academic Press | 2 edition (December 10, 2003) | ISBN: 0124725317 | Pages: 342 | PDF | 7.44 MB

Statistics in Spectroscopy, Second Edition, is an expanded and updated version of the original title. The aim of the book is to bridge the gap between the average chemist/spectroscopist and the study of statistics. This second edition differs from the first in that expanded chapters are incorporated to highlight the relationship between elementary statistics and the more advanced concepts of chemometrics.

The book introduces the novice reader to the ideas and concepts of statistics and uses spectroscopic examples to show how these concepts are applied. The advent of instrumentation and methods of data analysis based on multivariate mathematics has created a need to introduce the non-statitician to the ideas, concepts and thought processes of statistics and statisticians. Several key statistical concepts are introduced through the use of computer programs. The new sections on chemometrics include an exercise showing that there is a deep and fundamental connection between the two, supposedly different, disciplines of statistics and chemometrics.

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