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| handbook of computational statistics concepts and methods: Handbook of Computational Statistics James E. Gentle, Wolfgang Karl Härdle, Yuichi Mori, 2012-07-06 The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with How Computational Statistics became the backbone of modern data science (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications. |
| handbook of computational statistics concepts and methods: Computational Statistics Handbook with MATLAB Wendy L. Martinez, Angel R. Martinez, 2007-12-20 As with the bestselling first edition, Computational Statistics Handbook with MATLAB, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as |
| handbook of computational statistics concepts and methods: Handbook of Partial Least Squares Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler, Huiwen Wang, 2010-03-10 This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology. |
| handbook of computational statistics concepts and methods: Handbook of Computational Finance Jin-Chuan Duan, Wolfgang Karl Härdle, James E. Gentle, 2011-10-25 Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools. |
| handbook of computational statistics concepts and methods: Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications , 2018-08-27 Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more. The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important. - Provides a thorough treatment of open-source libraries, application frameworks and workflow systems for natural language analysis and understanding - Presents new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, and more |
| handbook of computational statistics concepts and methods: Computational Statistics Handbook with MATLAB Wendy L. Martinez, Angel R. Martinez, 2001-09-26 Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience with its implementation, and practice with |
| handbook of computational statistics concepts and methods: Computational Statistics in Data Science Walter W. Piegorsch, Richard A. Levine, Hao Helen Zhang, Thomas C. M. Lee, 2022-03-23 Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen. |
| handbook of computational statistics concepts and methods: Handbook of Parallel Computing and Statistics Erricos John Kontoghiorghes, 2005-12-21 Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts |
| handbook of computational statistics concepts and methods: Computational Statistics Geof H. Givens, Jennifer A. Hoeting, 2012-11-06 This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice. |
| handbook of computational statistics concepts and methods: Handbook of Computational Statistics James E. Gentle, Wolfgang Karl Härdle, Yuichi Mori, 2012-07-06 The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with How Computational Statistics became the backbone of modern data science (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications. |
| handbook of computational statistics concepts and methods: Computational Statistics James E. Gentle, 2009-07-28 Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. |
| handbook of computational statistics concepts and methods: Handbook of Design and Analysis of Experiments Angela Dean, Max Morris, John Stufken, Derek Bingham, 2015-06-26 This carefully edited collection synthesizes the state of the art in the theory and applications of designed experiments and their analyses. It provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook covers many recent advances in the field, including designs for nonlinear models and algorithms applicable to a wide variety of design problems. It also explores the extensive use of experimental designs in marketing, the pharmaceutical industry, engineering and other areas. |
| handbook of computational statistics concepts and methods: Handbook of Statistics for Teaching and Research in Plant and Crop Science Usha Palaniswamy, 2005-12-08 More than a textbook—it’s also a valuable reference book for researchers and crop science professionals! The Handbook of Statistics for Teaching and Research in Plant and Crop Science presents the fundamental concepts of important statistical methods and experimental designs to the students and researchers who need to apply them to their own specific problems. This comprehensive handbook takes what can be the difficult and confusing topics of statistics and experimental design and explains them in easily understandable terms, making them accessible to nearly every reader. More than a student textbook, it is an essential reference for researchers and professionals in a multitude of fields. Designed as a two-semester statistical textbook, the first section of the Handbook of Statistics for Teaching and Research in Plant and Crop Science focuses on statistical concepts, providing a foundation of useful knowledge on which you can base your own research. The second section concentrates on experimental designs in plant and crop sciences. The material is presented in a way that helps readers with a minimum of mathematical background to understand important theories and concepts. Derivations of formulas are avoided, and mathematical symbols are used only when essential. To illustrate the computational procedures, data is drawn from actual experiments. At the end of each chapter, examples and exercises are given to provide clear insight into real-life problems. A comprehensive appendix of clearly presented statistical tables is included. Part One of Handbook of Statistics for Teaching and Research in Plant and Crop Science focuses on statistical methods, principles, and procedures, exploring: methods of display of statistical information, such as tables, diagrams, graphs, etc. symbols and their use in denoting variables descriptions of types of statistical data methods of computation from raw and graphed data the importance of studying variables and dispersion in research the use of normal probability integral tables and their application to practical problems descriptions of different types of experiments, such as determinate and nondeterminate the significance of expected value in research special techniques in descriptive statistics explanations of population, sample, and statistical inference the significance of null hypothesis in research methods of correlation studies assumptions and principles in regression analysis Part Two concentrates on experimental design, principles and procedures, exploring: basic principles of experimental design the fundamental concepts of linear models and analysis of variance method and layout of Completely Randomized Design (CRD) the advantages and disadvantages of Randomized Complete Block Design (RCBD) methods and procedures for comparison of several treatment means the important features of Latin Square Design factorial experiments split plot design completely confounded design analysis of covariance the Chi Square Test of Significance the transformation of experimental data quality control and so much more! The Handbook of Statistics for Teaching and Research in Plant and Crop Science serves not only as a textbook for instructors and students in experimental design and statistics but also as a reference book on plant and crop sciences for professionals and researchers. The comprehensive text is also useful for professionals in other statistic-heavy fields. |
| handbook of computational statistics concepts and methods: Handbook of Statistical Bioinformatics Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao, 2022-12-08 Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology. |
| handbook of computational statistics concepts and methods: Handbook of Statistical Analysis and Data Mining Applications Robert Nisbet, John Elder, Gary D. Miner, 2009-05-14 The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. - Written By Practitioners for Practitioners - Non-technical explanations build understanding without jargon and equations - Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models - Practical advice from successful real-world implementations - Includes extensive case studies, examples, MS PowerPoint slides and datasets - CD-DVD with valuable fully-working 90-day software included: Complete Data Miner - QC-Miner - Text Miner bound with book |
| handbook of computational statistics concepts and methods: All of Statistics Larry Wasserman, 2004-09-17 This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. |
| handbook of computational statistics concepts and methods: Statistical Analysis Handbook Dr Michael John de Smith, A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools. |
| handbook of computational statistics concepts and methods: The Oxford Handbook of Computational and Mathematical Psychology Jerome R. Busemeyer, Zheng Wang, James T. Townsend, Ami Eidels, 2015-03-20 This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience. The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition). The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology--including cognitive science and related social and behavioral sciences such as consumer behavior and communication--will find the text useful. |
| handbook of computational statistics concepts and methods: Algorithms and Theory of Computation Handbook, Volume 1 Mikhail J. Atallah, Marina Blanton, 2009-11-20 Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many |
| handbook of computational statistics concepts and methods: Handbook of Probabilistic Models Pijush Samui, Dieu Tien Bui, Subrata Chakraborty, Ravinesh Deo, 2019-10-05 Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems |
| handbook of computational statistics concepts and methods: A Handbook of Statistical Analyses using R Torsten Hothorn, 2014-05-30 Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.New to the Third Edition |
| handbook of computational statistics concepts and methods: Statistical Computing with R Maria L. Rizzo, 2007-11-15 Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona |
| handbook of computational statistics concepts and methods: A Conceptual Guide to Statistics Using SPSS Elliot T. Berkman, Steven P. Reise, 2011-04-12 Bridging an understanding of Statistics and SPSS. The text is written in a user-friendly language and illustrates concepts that would otherwise be confusing to beginning students and those with limited computer skills. -Justice Mbizo, University of West Florida This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual underpinnings of the test. By drawing clear connections between the theoretical and computational aspects of statistics, this engaging text aids students′ understanding of theoretical concepts by teaching them in a practical context. |
| handbook of computational statistics concepts and methods: Computational Methods for Physicists Simon Sirca, Martin Horvat, 2012-12-17 This book helps advanced undergraduate, graduate and postdoctoral students in their daily work by offering them a compendium of numerical methods. The choice of methods pays significant attention to error estimates, stability and convergence issues as well as to the ways to optimize program execution speeds. Many examples are given throughout the chapters, and each chapter is followed by at least a handful of more comprehensive problems which may be dealt with, for example, on a weekly basis in a one- or two-semester course. In these end-of-chapter problems the physics background is pronounced, and the main text preceding them is intended as an introduction or as a later reference. Less stress is given to the explanation of individual algorithms. It is tried to induce in the reader an own independent thinking and a certain amount of scepticism and scrutiny instead of blindly following readily available commercial tools. |
| handbook of computational statistics concepts and methods: Computational Statistics , 2010-04-29 Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. |
| handbook of computational statistics concepts and methods: Methods and Models in Statistics Niall M. Adams, 2004 John Nelder was one of the most influential statisticians of his generation, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage. |
| handbook of computational statistics concepts and methods: Statistical Analysis and Data Display Richard M. Heiberger, Burt Holland, 2013-06-29 1 Audience Students seeking master's degrees in applied statistics in the late 1960s and 1970s typically took a year-long sequence in statistical methods. Popular choices of the course text book in that period prior to the availability of high speed computing and graphics capability were those authored by Snedecor and Cochran, and Steel and Torrie. By 1980, the topical coverage in these classics failed to include a great many new and important elementary techniques in the data analyst's toolkit. In order to teach the statistical methods sequence with adequate coverage of topics, it became necessary to draw material from each of four or five text sources. Obviously, such a situation makes life difficult for both students and instructors. In addition, statistics students need to become proficient with at least one high-quality statistical software package. This book can serve as a standalone text for a contemporary year-long course in statistical methods at a level appropriate for statistics majors at the master's level or other quantitatively oriented disciplines at the doctoral level. The topics include both concepts and techniques developed many years ago and a variety of newer tools not commonly found in textbooks. |
| handbook of computational statistics concepts and methods: Handbook of Computational Econometrics David A. Belsley, Erricos Kontoghiorghes, 2009-08-18 Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization, and estimation. Each topic is fully introduced before proceeding to a more in-depth examination of the relevant methodologies and valuable illustrations. This book: Provides self-contained treatments of issues in computational econometrics with illustrations and invaluable bibliographies. Brings together contributions from leading researchers. Develops the techniques needed to carry out computational econometrics. Features network studies, non-parametric estimation, optimization techniques, Bayesian estimation and inference, testing methods, time-series analysis, linear and nonlinear methods, VAR analysis, bootstrapping developments, signal extraction, software history and evaluation. This book will appeal to econometricians, financial statisticians, econometric researchers and students of econometrics at both graduate and advanced undergraduate levels. |
| handbook of computational statistics concepts and methods: Handbook of Computational Methods for Integration Prem K. Kythe, Michael R. Schäferkotter, 2004-12-20 During the past 20 years, there has been enormous productivity in theoretical as well as computational integration. Some attempts have been made to find an optimal or best numerical method and related computer code to put to rest the problem of numerical integration, but the research is continuously ongoing, as this problem is still very much open-ended. The importance of numerical integration in so many areas of science and technology has made a practical, up-to-date reference on this subject long overdue. The Handbook of Computational Methods for Integration discusses quadrature rules for finite and infinite range integrals and their applications in differential and integral equations, Fourier integrals and transforms, Hartley transforms, fast Fourier and Hartley transforms, Laplace transforms and wavelets. The practical, applied perspective of this book makes it unique among the many theoretical books on numerical integration and quadrature. It will be a welcomed addition to the libraries of applied mathematicians, scientists, and engineers in virtually every discipline. |
| handbook of computational statistics concepts and methods: Matrix Algebra James E. Gentle, 2007-07-27 Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. |
| handbook of computational statistics concepts and methods: The Cambridge Handbook of Computational Psychology Ron Sun, 2008-04-28 A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling. |
| handbook of computational statistics concepts and methods: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| handbook of computational statistics concepts and methods: The Cambridge Handbook of Computing Education Research Sally A. Fincher, Anthony V. Robins, 2019-02-13 This is an authoritative introduction to Computing Education research written by over 50 leading researchers from academia and the industry. |
| handbook of computational statistics concepts and methods: Algorithms and Theory of Computation Handbook, Volume 2 Mikhail J. Atallah, Marina Blanton, 2009-11-20 Algorithms and Theory of Computation Handbook, Second Edition: Special Topics and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems.Along with updating and revising many of |
| handbook of computational statistics concepts and methods: Learning Statistics with R Daniel Navarro, 2013-01-13 Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com |
| handbook of computational statistics concepts and methods: Experimental Statistics Mary Gibbons Natrella, 2005-02-28 A handbook for those seeking engineering information and quantitative data for designing, developing, constructing, and testing equipment. Covers the planning of experiments, the analyzing of extreme-value data; and more. 1966 edition. Index. Includes 52 figures and 76 tables. |
| handbook of computational statistics concepts and methods: Computational Statistical Methodologies and Modeling for Artificial Intelligence Priyanka Harjule, Azizur Rahman, Basant Agarwal, Vinita Tiwari, 2023-03-31 This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence |
| handbook of computational statistics concepts and methods: Fundamentals of Applied Statistics and Surveys David .B. Orr, 1995-04-01 As the world becomes increasingly complex, that complexity tends to be reflected in statistical approaches to things that comprise our everyday experiences. Many people now find that understanding statistics is a skill they need to succeed in the many disciplines and occupations that rely on data and numerical information. While they may not need computational skills, rigorous mathematics, or the ability to actually design studies themselves, they do need some background in the concepts essential to understanding and intelligently applying common statistical procedures and ideas. Fundamentals of Applied Statistics and Surveys provides that background. Heavily focused on conceptual content, its approach is discursive rather than computational. The author emphasizes the more easily understandable, and more generally useful, techniques most frequently encountered by those in careers other than statistics. Aimed at users of statistical information, rather than producers of it, Fundamentals of Applied Statistics and Surveys provides an accessible treatment of basic concepts and methods without involving the reader in extensive mathematics or complicated computations. |
| handbook of computational statistics concepts and methods: R for Statistics Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, Laurent Rouvière, 2012-03-21 Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples. Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. Focusing on the R software, the first section covers: Basic elements of the R software and data processing Clear, concise visualization of results, using simple and complex graphs Programming basics: pre-defined and user-created functions The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including: Regression methods Analyses of variance and covariance Classification methods Exploratory multivariate analysis Clustering methods Hypothesis tests After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist. Datasets and all the results described in this book are available on the book’s webpage at http://www.agrocampus-ouest.fr/math/RforStat |
| handbook of computational statistics concepts and methods: Statistics in MATLAB MoonJung Cho, Wendy L. Martinez, 2014-12-15 This primer provides an accessible introduction to MATLAB version 8 and its extensive functionality for statistics. Fulfilling the need for a practical user's guide, the book covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB, presenting examples of how MATLAB can be used to analyze data. It explains how to determine what method should be used for analysis, and includes figures, visual aids, and access to a companion website with data sets and additional examples. |
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Blusa Barra Assimétrica Off White | Handbook
Blusa Handbook confeccionada em malha trabalhada com detalhe de lurex. Sua modelagem cropped, gola alta, frente com recorte orgânico e barra assimétrica, manga longa punho com …
Vestido Recorte Sensualite - Handbook Online
Vestido Handbook confeccionada em tecido de malha plissada com brilho. Sua modelagem justa, decote redondo, manga longa, recorte vazado na cintura com fita para regulagem, com barra …
Blusa Decote Canoa Bege | Handbook
Blusa Handbook confeccionada em renda vazada com detalhes de paetê. Com modelagem justa, gola canoa, com forro solto no busto, mangas longas e barra reta. Perfeita para curtir uma …
Saia Transpassada Detalhe De Ilhós Cinza | Handbook
Saia Handbook confeccionada em malha encorpada acetinada. Com modelagem evasê de cintura alta, apresenta frente dupla transpassada com detalhes em ilhós na cor níquel e …
Jaqueta Parka Issey Preto | Handbook
Jaqueta Handbook confeccionado em tecido com imitação de couro plissado. Sua modelagem parka, abertura frontal com vista larga, cintura modelada com faixa para amarração. Super …
Vestido Longo Fenda Flower Estampado | Handbook
Vestido Handbook confeccionado em tule estampado com transparência, este vestido apresenta modelagem justa e comprimento longo. O decote assimétrico, com uma alça única, e a linda …
Pochete Handbook Polonia Preto | Handbook
Se antes muitos caras tinham certo preconceito com a pochete , hoje em uma releitura caiu nas garça da Handbook para os caras mais estiloso ou para aqueles que procuram comodidade, …
Jaqueta Capuz Dil - Handbook Online
Jaqueta Handbook confeccionada em tecido de nylon. Sua modelagem comprimento mais curto, caimento solto ao corpo, bolso frontal com detalhe de zíper, capuz ajustável, abertura frontal …
Calça Jogger Acetinado Off White | Handbook
Calça Handbook confeccionada em tecido plano com um toque acetinado. Com modelagem jogger, possui cós tradicional com passantes, pregas frontais e barra da perna com punho …
Vestido Tubinho Adriana Preto | Handbook
Vestido da Handbook confeccionado em malha trabalhada, com modelagem justa que valoriza a silhueta. O design conta com gola alta e fechamento em botão na nuca, garantindo um ajuste …
Blusa Barra Assimétrica Off White | Handbook
Blusa Handbook confeccionada em malha trabalhada com detalhe de lurex. Sua modelagem cropped, gola alta, frente com recorte orgânico e barra assimétrica, manga longa punho com …
Vestido Recorte Sensualite - Handbook Online
Vestido Handbook confeccionada em tecido de malha plissada com brilho. Sua modelagem justa, decote redondo, manga longa, recorte vazado na cintura com fita para regulagem, com barra …
Blusa Decote Canoa Bege | Handbook
Blusa Handbook confeccionada em renda vazada com detalhes de paetê. Com modelagem justa, gola canoa, com forro solto no busto, mangas longas e barra reta. Perfeita para curtir uma …
Saia Transpassada Detalhe De Ilhós Cinza | Handbook
Saia Handbook confeccionada em malha encorpada acetinada. Com modelagem evasê de cintura alta, apresenta frente dupla transpassada com detalhes em ilhós na cor níquel e …
Jaqueta Parka Issey Preto | Handbook
Jaqueta Handbook confeccionado em tecido com imitação de couro plissado. Sua modelagem parka, abertura frontal com vista larga, cintura modelada com faixa para amarração. Super …
Vestido Longo Fenda Flower Estampado | Handbook
Vestido Handbook confeccionado em tule estampado com transparência, este vestido apresenta modelagem justa e comprimento longo. O decote assimétrico, com uma alça única, e a linda …
Pochete Handbook Polonia Preto | Handbook
Se antes muitos caras tinham certo preconceito com a pochete , hoje em uma releitura caiu nas garça da Handbook para os caras mais estiloso ou para aqueles que procuram comodidade, …
Jaqueta Capuz Dil - Handbook Online
Jaqueta Handbook confeccionada em tecido de nylon. Sua modelagem comprimento mais curto, caimento solto ao corpo, bolso frontal com detalhe de zíper, capuz ajustável, abertura frontal …
