An Introduction to Statistical Computing

A Simulation-based Approach
Author: Jochen Voss
Publisher: John Wiley & Sons
ISBN: 1118728025
Category: Mathematics
Page: 400
View: 2426

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A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: Fully covers the traditional topics of statistical computing. Discusses both practical aspects and the theoretical background. Includes a chapter about continuous-time models. Illustrates all methods using examples and exercises. Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course

Bilinear Regression Analysis

An Introduction
Author: Dietrich von Rosen
Publisher: Springer
ISBN: 3319787845
Category: Mathematics
Page: 468
View: 5723

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This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.

Uncertainty Management in Simulation-Optimization of Complex Systems

Algorithms and Applications
Author: Gabriella Dellino,Carlo Meloni
Publisher: Springer
ISBN: 1489975470
Category: Business & Economics
Page: 271
View: 896

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​This book aims at illustrating strategies to account for uncertainty in complex systems described by computer simulations. When optimizing the performances of these systems, accounting or neglecting uncertainty may lead to completely different results; therefore, uncertainty management is a major issues in simulation-optimization. Because of its wide field of applications, simulation-optimization issues have been addressed by different communities with different methods, and from slightly different perspectives. Alternative approaches have been developed, also depending on the application context, without any well-established method clearly outperforming the others. This editorial project brings together — as chapter contributors — researchers from different (though interrelated) areas; namely, statistical methods, experimental design, stochastic programming, global optimization, metamodeling, and design and analysis of computer simulation experiments. Editors’ goal is to take advantage of such a multidisciplinary environment, to offer to the readers a much deeper understanding of the commonalities and differences of the various approaches to simulation-based optimization, especially in uncertain environments. Editors aim to offer a bibliographic reference on the topic, enabling interested readers to learn about the state-of-the-art in this research area, also accounting for potential real-world applications to improve also the state-of-the-practice. Besides researchers and scientists of the field, the primary audience for the proposed book includes PhD students, academic teachers, as well as practitioners and professionals. Each of these categories of potential readers present adequate channels for marketing actions, e.g. scientific, academic or professional societies, internet-based communities, and authors or buyers of related publications.​

Statistical Pattern Recognition

Author: Andrew R. Webb
Publisher: John Wiley & Sons
ISBN: 0470854782
Category: Mathematics
Page: 514
View: 2973

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Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. * Provides a self-contained introduction to statistical pattern recognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. * Each section concludes with a description of the applications that have been addressed and with further developments of the theory. * Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. For further information on the techniques and applications discussed in this book please visit

Elements of Computational Statistics

Author: James E. Gentle
Publisher: Springer Science & Business Media
ISBN: 0387954899
Category: Computers
Page: 420
View: 4041

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In computational statistics, computation is viewed as an instrument of discovery. The role of the computer is not just to store data, perform computations, and produce graphs and tables, but additionally to suggest to the scientist alternative models and theories. This book describes techniques used in computational statistics and considers some of the areas of application, such as density estimation and model building, in which computationally-intensive methods are useful.

Random Graphs for Statistical Pattern Recognition

Author: David J. Marchette
Publisher: John Wiley & Sons
ISBN: 9780471722083
Category: Mathematics
Page: 264
View: 8875

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A timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the firstbook to address the topic of random graphs as it applies tostatistical pattern recognition. Both topics are of vital interestto researchers in various mathematical and statistical fields andhave never before been treated together in one book. The use ofdata random graphs in pattern recognition in clustering andclassification is discussed, and the applications for bothdisciplines are enhanced with new tools for the statistical patternrecognition community. New and interesting applications for randomgraph users are also introduced. This important addition to statistical literaturefeatures: Information that previously has been available only throughscattered journal articles Practical tools and techniques for a wide range of real-worldapplications New perspectives on the relationship between patternrecognition and computational geometry Numerous experimental problems to encourage practicalapplications With its comprehensive coverage of two timely fields, enhancedwith many references and real-world examples, Random Graphs forStatistical Pattern Recognition is a valuable resource forindustry professionals and students alike.

Statistical and Machine Learning Approaches for Network Analysis

Author: Matthias Dehmer,Subhash C. Basak
Publisher: John Wiley & Sons
ISBN: 111834698X
Category: Mathematics
Page: 344
View: 6381

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Explore the multidisciplinary nature of complex networksthrough machine learning techniques Statistical and Machine Learning Approaches for NetworkAnalysis provides an accessible framework for structurallyanalyzing graphs by bringing together known and novel approaches ongraph classes and graph measures for classification. By providingdifferent approaches based on experimental data, the book uniquelysets itself apart from the current literature by exploring theapplication of machine learning techniques to various types ofcomplex networks. Comprised of chapters written by internationally renownedresearchers in the field of interdisciplinary network theory, thebook presents current and classical methods to analyze networksstatistically. Methods from machine learning, data mining, andinformation theory are strongly emphasized throughout. Real datasets are used to showcase the discussed methods and topics, whichinclude: A survey of computational approaches to reconstruct andpartition biological networks An introduction to complex networks—measures, statisticalproperties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for NetworkAnalysis is an excellent supplemental text for graduate-level,cross-disciplinary courses in applied discrete mathematics,bioinformatics, pattern recognition, and computer science. The bookis also a valuable reference for researchers and practitioners inthe fields of applied discrete mathematics, machine learning, datamining, and biostatistics.

Understanding Computational Bayesian Statistics

Author: William M. Bolstad
Publisher: John Wiley & Sons
ISBN: 0470046090
Category: Mathematics
Page: 315
View: 8795

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A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution The distributions from the one-dimensional exponential family Markov chains and their long-run behavior The Metropolis-Hastings algorithm Gibbs sampling algorithm and methods for speeding up convergence Markov chain Monte Carlo sampling Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages. Understanding Computational Bayesian Statistics is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.

Random processes in physical systems

an introduction to probability-based computer simulations
Author: Charles Allen Whitney
Publisher: Wiley-Interscience
Category: Science
Page: 320
View: 5258

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Introduces the reader to applications of computer programs that permit the manipulation of simulated physical systems, unlocking the potential for dramatic insights in the fields of physics, chemistry and statistics. Divided into four sections, it opens with an introduction to pseudo-random numbers and discusses the concept of the ''random walk'' as well as the excitation of atoms whose energy arrives in discrete quanta. Sample listings of computer programs for some of the key calculations are included. Section 2 describes a few of the most important processes that take place in the continuum of time, especially the scattering of photons in a gas and the ''Brownian motion'' of small particles. The third section applies these modeling techniques to the behavior of more complex systems and points the way to what promises to be a major use of computers in the future. Section 4 introduces the application of randomizing methods to the solution of statistical problems such as curve-fitting and error analysis. Using computer methods modeled on the rules of gambling, it promises to be a milestone in the field of physics education.

Weibull Models

Author: D. N. Prabhakar Murthy,Min Xie,Renyan Jiang
Publisher: John Wiley & Sons
ISBN: 9780471473275
Category: Mathematics
Page: 408
View: 801

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A comprehensive perspective on Weibull models The literature on Weibull models is vast, disjointed, andscattered across many different journals. Weibull Models is acomprehensive guide that integrates all the different facets ofWeibull models in a single volume. This book will be of great help to practitioners in reliabilityand other disciplines in the context of modeling data sets usingWeibull models. For researchers interested in these modelingtechniques, exercises at the end of each chapter define potentialtopics for future research. Organized into seven distinct parts, Weibull Models: Covers model analysis, parameter estimation, model validation,and application Serves as both a handbook and a research monograph. As ahandbook, it classifies the different models and presents theirproperties. As a research monograph, it unifies the literature andpresents the results in an integrated manner Intertwines theory and application Focuses on model identification prior to model parameterestimation Discusses the usefulness of the Weibull Probability plot (WPP)in the model selection to model a given data set Highlights the use of Weibull models in reliability theory Filled with in-depth analysis, Weibull Models pulls together themost relevant information on this topic to give everyone fromreliability engineers to applied statisticians involved withreliability and survival analysis a clear look at what Weibullmodels can offer.

Large-Scale Inverse Problems and Quantification of Uncertainty

Author: Lorenz Biegler,George Biros,Omar Ghattas,Matthias Heinkenschloss,David Keyes,Bani Mallick,Luis Tenorio,Bart van Bloemen Waanders,Karen Willcox,Youssef Marzouk
Publisher: John Wiley & Sons
ISBN: 1119957583
Category: Mathematics
Page: 388
View: 3920

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This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: Brings together the perspectives of researchers in areas of inverse problems and data assimilation. Assesses the current state-of-the-art and identify needs and opportunities for future research. Focuses on the computational methods used to analyze and simulate inverse problems. Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.

Nonparametric Statistical Methods

Author: Myles Hollander,Douglas A. Wolfe,Eric Chicken
Publisher: John Wiley & Sons
ISBN: 1118553292
Category: Mathematics
Page: 848
View: 2903

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Praise for the Second Edition “This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation. Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features: The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new edition New chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescience Nonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics.

Numerical Methods in Finance

A MATLAB-Based Introduction
Author: Paolo Brandimarte
Publisher: John Wiley & Sons
ISBN: 0471461695
Category: Mathematics
Page: 432
View: 6041

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Balanced coverage of the methodology and theory of numericalmethods in finance Numerical Methods in Finance bridges the gap between financialtheory and computational practice while helping students andpractitioners exploit MATLAB for financial applications. Paolo Brandimarte covers the basics of finance and numericalanalysis and provides background material that suits the needs ofstudents from both financial engineering and economicsperspectives. Classical numerical analysis methods; optimization,including less familiar topics such as stochastic and integerprogramming; simulation, including low discrepancy sequences; andpartial differential equations are covered in detail. Extensiveillustrative examples of the application of all of thesemethodologies are also provided. The text is primarily focused on MATLAB-based application, but alsoincludes descriptions of other readily available toolboxes that arerelevant to finance. Helpful appendices on the basics of MATLAB andprobability theory round out this balanced coverage. Accessible forstudents-yet still a useful reference for practitioners-NumericalMethods in Finance offers an expert introduction to powerful toolsin finance.

Operations Research

Author: Frederick S. Hillier,Gerald J. Liebermann
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3486792083
Category: Business & Economics
Page: 868
View: 6165

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Aus dem Inhalt: Was ist Operations Research? Überblick über die Modellierungsgrundsätze des Operations Research. Einführung in die lineare Programmierung. Die Lösung linearer Programmierungsprobleme: Das Simplexverfahren. Stochastische Prozesse. Warteschlangentheorie. Lagerhaltungstheorie. Prognoseverfahren. Markov-Entscheidungsprozesse. Reliabilität. Entscheidungstheorie. Die Theorie des Simplexverfahrens Qualitätstheorie und Sensitivitätsanalyse Spezialfälle linearer Programmierungsprobleme. Die Formulierung linearer Programmierungsmodelle und Goal-Programmierung. Weitere Algorithmen der linearen Programmierung. Netzwerkanalyse einschließlich PERT-CPM. Dynamische Optimierung. Spieltheorie. Ganzzahlige Programmierung. Nichtlineare Programmierung Simulation. Anhang. Lösungen für ausgewählte Übungsaufgaben.