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Список книг автора Группа авторовProbability and Random Processes
A resource for probability AND random processes, with hundreds of worked examples and probability and Fourier transform tables This survival guide in probability and random processes eliminates the need to pore through several resources to find a certain formula or table. It offers a compendium of most distribution functions used by communication engineers, queuing theory specialists, signal processing engineers, biomedical engineers, physicists, and students. Key topics covered include: * Random variables and most of their frequently used discrete and continuous probability distribution functions * Moments, transformations, and convergences of random variables * Characteristic, generating, and moment-generating functions * Computer generation of random variates * Estimation theory and the associated orthogonality principle * Linear vector spaces and matrix theory with vector and matrix differentiation concepts * Vector random variables * Random processes and stationarity concepts * Extensive classification of random processes * Random processes through linear systems and the associated Wiener and Kalman filters * Application of probability in single photon emission tomography (SPECT) More than 400 figures drawn to scale assist readers in understanding and applying theory. Many of these figures accompany the more than 300 examples given to help readers visualize how to solve the problem at hand. In many instances, worked examples are solved with more than one approach to illustrate how different probability methodologies can work for the same problem. Several probability tables with accuracy up to nine decimal places are provided in the appendices for quick reference. A special feature is the graphical presentation of the commonly occurring Fourier transforms, where both time and frequency functions are drawn to scale. This book is of particular value to undergraduate and graduate students in electrical, computer, and civil engineering, as well as students in physics and applied mathematics. Engineers, computer scientists, biostatisticians, and researchers in communications will also benefit from having a single resource to address most issues in probability and random processes.
Survey Errors and Survey Costs
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. «Survey Errors and Survey Costs is a well-written, well-presented, and highly readable text that should be on every error-conscious statistician’s bookshelf. Any courses that cover the theory and design of surveys should certainly have Survey Errors and Survey Costs on their reading lists.» –Phil Edwards MEL, Aston University Science Park, UK Review in The Statistician, Vol. 40, No. 3, 1991 «This volume is an extremely valuable contribution to survey methodology. It has many virtues: First, it provides a framework in which survey errors can be segregated by sources. Second, Groves has skillfully synthesized existing knowledge, bringing together in an easily accessible form empirical knowledge from a variety of sources. Third, he has managed to integrate into a common framework the contributions of several disciplines. For example, the work of psychometricians and cognitive psychologists is made relevant to the research of econometricians as well as the field experience of sociologists. Finally, but not least, Groves has managed to present all this in a style that is accessible to a wide variety of readers ranging from survey specialists to policymakers.» –Peter H. Rossi University of Massachusetts at Amherst Review in Journal of Official Statistics, January 1991
Statistical Design for Research
The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. This title addresses those basic aspects of research design which are common to many related fields in the social sciences, health sciences, education, and market research. The work presents a unified approach to a common core of problems of statistical design that exists in all these fields, along with basic similarities in practical solutions. Describing many examples and analogies that are 'portable' from application field to application field, Statistical Design for Research deals with designs that are the primary basis of research studies, but are neglected in most statistical textbooks that tend to concentrate on statistical analysis. This text takes a broader, more general and philosophical view of the statistics for the more fundamental aspects of design than do the standard treatments of experimental design. Extensively illustrated and carefully organized into seven chapters and 44 sections, this book can be readily consulted by research workers or graduate students!
Regression With Social Data
An accessible introduction to the use of regression analysis in the social sciences Regression with Social Data: Modeling Continuous and Limited Response Variables represents the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners. Covering techniques that span the full spectrum of levels of measurement for both continuous and limited response variables, and using examples taken from such disciplines as sociology, psychology, political science, and public health, the author succeeds in demystifying an academically rigorous subject and making it accessible to a wider audience. Content includes coverage of: Logit, probit, scobit, truncated, and censored regressions Multiple regression with ANOVA and ANCOVA models Binary and multinomial response models Poisson, negative binomial, and other regression models for event-count data Survival analysis using multistate, multiepisode, and interval-censored survival models Concepts are reinforced throughout with numerous chapter problems, exercises, and real data sets. Step-by-step solutions plus an appendix of mathematical tutorials make even complex problems accessible to readers with only moderate math skills. The book’s logical flow, wide applicability, and uniquely comprehensive coverage make it both an ideal text for a variety of graduate course settings and a useful reference for practicing researchers in the field.
Numerical Methods in Finance
Balanced coverage of the methodology and theory of numerical methods in finance Numerical Methods in Finance bridges the gap between financial theory and computational practice while helping students and practitioners exploit MATLAB for financial applications. Paolo Brandimarte covers the basics of finance and numerical analysis and provides background material that suits the needs of students from both financial engineering and economics perspectives. Classical numerical analysis methods; optimization, including less familiar topics such as stochastic and integer programming; simulation, including low discrepancy sequences; and partial differential equations are covered in detail. Extensive illustrative examples of the application of all of these methodologies are also provided. The text is primarily focused on MATLAB-based application, but also includes descriptions of other readily available toolboxes that are relevant to finance. Helpful appendices on the basics of MATLAB and probability theory round out this balanced coverage. Accessible for students-yet still a useful reference for practitioners-Numerical Methods in Finance offers an expert introduction to powerful tools in finance.
Methodology of Longitudinal Surveys
Longitudinal surveys are surveys that involve collecting data from multiple subjects on multiple occasions. They are typically used for collecting data relating to social, economic, educational and health-related issues and they serve as an important tool for economists, sociologists, and other researchers. Focusing on the design, implementation and analysis of longitudinal surveys, Methodology of Longitudinal Surveys discusses the current state of the art in carrying out these surveys. The book also covers issues that arise in surveys that collect longitudinal data via retrospective methods. Aimed at researchers and practitioners analyzing data from statistical surveys the book will also be suitable as supplementary reading for graduate students of survey statistics. This book: Covers all the main stages in the design, implementation and analysis of longitudinal surveys. Reviews recent developments in the field, including the use of dependent interviewing and mixed mode data collection. Discusses the state of the art in sampling, weighting and non response adjustment. Features worked examples throughout using real data. Addresses issues arising from the collection of data via retrospective methods, as well as ethical issues, confidentiality and non-response bias. Is written by an international team of contributors consisting of some of the most respected Survey Methodology experts in the field
Markov Processes and Applications
This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an emphasis towards applications. The mathematical treatment is precise and rigorous without superfluous details, and the results are immediately illustrated in illuminating examples. This book will be extremely useful to anybody teaching a course on Markov processes. Jean-François Le Gall, Professor at Université de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally independent, given their present state. They constitute important models in many applied fields. After an introduction to the Monte Carlo method, this book describes discrete time Markov chains, the Poisson process and continuous time Markov chains. It also presents numerous applications including Markov Chain Monte Carlo, Simulated Annealing, Hidden Markov Models, Annotation and Alignment of Genomic sequences, Control and Filtering, Phylogenetic tree reconstruction and Queuing networks. The last chapter is an introduction to stochastic calculus and mathematical finance. Features include: The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes. An introduction to diffusion processes, mathematical finance and stochastic calculus. Applications of Markov processes to various fields, ranging from mathematical biology, to financial engineering and computer science. Numerous exercises and problems with solutions to most of them
Statistical Rules of Thumb
Praise for the First Edition: «For a beginner [this book] is a treasure trove; for an experienced person it can provide new ideas on how better to pursue the subject of applied statistics.» —Journal of Quality Technology Sensibly organized for quick reference, Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and each captures key statistical concepts. This unique guide to the use of statistics for designing, conducting, and analyzing research studies illustrates real-world statistical applications through examples from fields such as public health and environmental studies. Along with an insightful discussion of the reasoning behind every technique, this easy-to-use handbook also conveys the various possibilities statisticians must think of when designing and conducting a study or analyzing its data. Each chapter presents clearly defined rules related to inference, covariation, experimental design, consultation, and data representation, and each rule is organized and discussed under five succinct headings: introduction; statement and illustration of the rule; the derivation of the rule; a concluding discussion; and exploration of the concept's extensions. The author also introduces new rules of thumb for topics such as sample size for ratio analysis, absolute and relative risk, ANCOVA cautions, and dichotomization of continuous variables. Additional features of the Second Edition include: Additional rules on Bayesian topics New chapters on observational studies and Evidence-Based Medicine (EBM) Additional emphasis on variation and causation Updated material with new references, examples, and sources A related Web site provides a rich learning environment and contains additional rules, presentations by the author, and a message board where readers can share their own strategies and discoveries. Statistical Rules of Thumb, Second Edition is an ideal supplementary book for courses in experimental design and survey research methods at the upper-undergraduate and graduate levels. It also serves as an indispensable reference for statisticians, researchers, consultants, and scientists who would like to develop an understanding of the statistical foundations of their research efforts. A related website www.vanbelle.org provides additional rules, author presentations and more.
Statistical Modeling by Wavelets
A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for «time-scale» applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly «discovered» mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, Vidakovic compiles, organizes, and explains in depth research data previously available only in disparate journal articles. He carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, and extensive references-with data sets and S-Plus wavelet overviews made available for downloading over the Internet. Both introductory and data-oriented modeling topics are featured, including: * Continuous and discrete wavelet transformations. * Statistical optimality properties of wavelet shrinkage. * Theoretical aspects of wavelet density estimation. * Bayesian modeling in the wavelet domain. * Properties of wavelet-based random functions and densities. * Several novel and important wavelet applications in statistics. * Wavelet methods in time series. Accessible to anyone with a background in advanced calculus and algebra, Statistical Modeling by Wavelets promises to become the standard reference for statisticians and engineers seeking a comprehensive introduction to an emerging field.
Convergence of Probability Measures
A new look at weak-convergence methods in metric spaces-from a master of probability theory In this new edition, Patrick Billingsley updates his classic work Convergence of Probability Measures to reflect developments of the past thirty years. Widely known for his straightforward approach and reader-friendly style, Dr. Billingsley presents a clear, precise, up-to-date account of probability limit theory in metric spaces. He incorporates many examples and applications that illustrate the power and utility of this theory in a range of disciplines-from analysis and number theory to statistics, engineering, economics, and population biology. With an emphasis on the simplicity of the mathematics and smooth transitions between topics, the Second Edition boasts major revisions of the sections on dependent random variables as well as new sections on relative measure, on lacunary trigonometric series, and on the Poisson-Dirichlet distribution as a description of the long cycles in permutations and the large divisors of integers. Assuming only standard measure-theoretic probability and metric-space topology, Convergence of Probability Measures provides statisticians and mathematicians with basic tools of probability theory as well as a springboard to the «industrial-strength» literature available today.