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N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance
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N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance


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7884 руб.

Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including the following: Use of the software SAS, in addition to GenStat and R. Meta-analysis and the multiple testing problem. The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: “One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models…Each chapter is well-motivated by at least one carefully chosen example…demonstrating the broad applicability of mixed models in many different disciplines…most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.”—Kerrie Nelson University of South Carolina, in American Statistician, 2007

N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance
:

N. Galwey W. Introduction to Mixed Modelling. Beyond Regression and Analysis of Variance


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8090.66 руб.

Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including the following: Use of the software SAS, in addition to GenStat and R. Meta-analysis and the multiple testing problem. The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: “One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models…Each chapter is well-motivated by at least one carefully chosen example…demonstrating the broad applicability of mixed models in many different disciplines…most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.”—Kerrie Nelson University of South Carolina, in American Statistician, 2007

Douglas Montgomery C. Solutions Manual to Accompany Introduction to Linear Regression Analysis Douglas Montgomery C. Solutions Manual to Accompany Introduction to Linear Regression Analysis
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Douglas Montgomery C. Solutions Manual to Accompany Introduction to Linear Regression Analysis


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2387.37 руб.

As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.

Douglas Montgomery C. Introduction to Linear Regression Analysis Douglas Montgomery C. Introduction to Linear Regression Analysis
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Douglas Montgomery C. Introduction to Linear Regression Analysis


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10921.91 руб.

Praise for the Fourth Edition «As with previous editions, the authors have produced a leading textbook on regression.» —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

Michael Crawley J. Statistics. An Introduction Using R Michael Crawley J. Statistics. An Introduction Using R
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Michael Crawley J. Statistics. An Introduction Using R


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3399.54 руб.

"…I know of no better book of its kind…" (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t–tests and chi–squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.

Simon James Dadson Statistical Analysis of Geographical Data. An Introduction Simon James Dadson Statistical Analysis of Geographical Data. An Introduction
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Simon James Dadson Statistical Analysis of Geographical Data. An Introduction


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2889.68 руб.

Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the reader’s understanding.

Machin David Regression Methods for Medical Research Machin David Regression Methods for Medical Research
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Machin David Regression Methods for Medical Research


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5785.94 руб.

Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures. The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the key design questions posed and in so doing take due account of any effects of potentially influencing co-variables. It begins with a revision of basic statistical concepts, followed by a gentle introduction to the principles of statistical modelling. The various methods of modelling are covered in a non-technical manner so that the principles can be more easily applied in everyday practice. A chapter contrasting regression modelling with a regression tree approach is included. The emphasis is on the understanding and the application of concepts and methods. Data drawn from published studies are used to exemplify statistical concepts throughout. Regression Methods for Medical Research is especially designed for clinicians, public health and environmental health professionals, para-medical research professionals, scientists, laboratory-based researchers and students.

Sanford Weisberg Applied Linear Regression Sanford Weisberg Applied Linear Regression
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Sanford Weisberg Applied Linear Regression


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10125.89 руб.

Praise for the Third Edition «…this is an excellent book which could easily be used as a course text…» —International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition features: Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, and variance assumptions Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.

David Hampton Hedge Fund Modelling and Analysis using MATLAB David Hampton Hedge Fund Modelling and Analysis using MATLAB
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David Hampton Hedge Fund Modelling and Analysis using MATLAB


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5876.24 руб.

The second book in Darbyshire and Hampton’s Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB® takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB®. This allows for a more detailed analysis of some of the more computationally intensive and advanced topics, such as hedge fund classification, performance measurement and mean-variance optimisation. Darbyshire and Hampton’s first book in the series, Hedge Fund Modelling and Analysis Using Excel & and VBA, is seen as a valuable supplementary text to this book. Starting with an overview of the hedge fund industry the book then looks at a variety of commercially available hedge fund data sources. After covering key statistical techniques and methods, the book discusses mean-variance optimisation, hedge fund classification and performance with an emphasis on risk-adjusted return metrics. Finally, common hedge fund market risk management techniques, such as traditional Value-at-Risk methods, modified extensions and expected shortfall are covered. The book’s dedicated website, www.darbyshirehampton.com provides free downloads of all the data and MATLAB® source code, as well as other useful resources. Hedge Fund Modelling and Analysis Using MATLAB® serves as a definitive introductory guide to hedge fund modelling and analysis and will provide investors, industry practitioners and students alike with a useful range of tools and techniques for analysing and estimating alpha and beta sources of return, performing manager ranking and market risk management.

Samprit Chatterjee Handbook of Regression Analysis Samprit Chatterjee Handbook of Regression Analysis
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Samprit Chatterjee Handbook of Regression Analysis


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9909.05 руб.

A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis. Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of regression methods, but it has been deliberately written at an accessible level. The handbook provides a quick and convenient reference or “refresher” on ideas and methods that are useful for the effective analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (including linear, binary logistic, multinomial logistic, count, and nonlinear regression models). Theory underlying the methodology is presented when it advances conceptual understanding and is always supplemented by hands-on examples. References are supplied for readers wanting more detailed material on the topics discussed in the book. R code and data for all of the analyses described in the book are available via an author-maintained website. «I enjoyed the presentation of the Handbook, and I would be happy to recommend this nice handy book as a reference to my students. The clarity of the writing and proper choices of examples allows the presentations ofmany statisticalmethods shine. The quality of the examples at the end of each chapter is a strength. They entail explanations of the resulting R outputs and successfully guide readers to interpret them.» American Statistician

Karl-Gunnar Olsson Structural Mechanics: Modelling and Analysis of Frames and Trusses Karl-Gunnar Olsson Structural Mechanics: Modelling and Analysis of Frames and Trusses
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Karl-Gunnar Olsson Structural Mechanics: Modelling and Analysis of Frames and Trusses


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4347.9 руб.

Textbook covers the fundamental theory of structural mechanics and the modelling and analysis of frame and truss structures Deals with modelling and analysis of trusses and frames using a systematic matrix formulated displacement method with the language and flexibility of the finite element method Element matrices are established from analytical solutions to the differential equations Provides a strong toolbox with elements and algorithms for computational modelling and numerical exploration of truss and frame structures Discusses the concept of stiffness as a qualitative tool to explain structural behaviour Includes numerous exercises, for some of which the computer software CALFEM is used. In order to support the learning process CALFEM gives the user full overview of the matrices and algorithms used in a finite element analysis

Hadi Ali S. Regression Analysis by Example Hadi Ali S. Regression Analysis by Example
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Hadi Ali S. Regression Analysis by Example


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9872.09 руб.

Praise for the Fourth Edition: «This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable.» —Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression Fitting nonlinear models Errors in variables ANOVA for designed experiments Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.

A. K. Md. Ehsanes Saleh An Introduction to Probability and Statistics A. K. Md. Ehsanes Saleh An Introduction to Probability and Statistics
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A. K. Md. Ehsanes Saleh An Introduction to Probability and Statistics


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9764.94 руб.

A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Iain Pardoe Applied Regression Modeling Iain Pardoe Applied Regression Modeling
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Iain Pardoe Applied Regression Modeling


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9571.47 руб.

Praise for the First Edition «The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful.» —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.

Kerrie Mengersen L. Case Studies in Bayesian Statistical Modelling and Analysis Kerrie Mengersen L. Case Studies in Bayesian Statistical Modelling and Analysis
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Kerrie Mengersen L. Case Studies in Bayesian Statistical Modelling and Analysis


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7595.11 руб.

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

Daniel Arrigo J. Symmetry Analysis of Differential Equations. An Introduction Daniel Arrigo J. Symmetry Analysis of Differential Equations. An Introduction
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Daniel Arrigo J. Symmetry Analysis of Differential Equations. An Introduction


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6654.29 руб.

A self-contained introduction to the methods and techniques of symmetry analysis used to solve ODEs and PDEs Symmetry Analysis of Differential Equations: An Introduction presents an accessible approach to the uses of symmetry methods in solving both ordinary differential equations (ODEs) and partial differential equations (PDEs). Providing comprehensive coverage, the book fills a gap in the literature by discussing elementary symmetry concepts and invariance, including methods for reducing the complexity of ODEs and PDEs in an effort to solve the associated problems. Thoroughly class-tested, the author presents classical methods in a systematic, logical, and well-balanced manner. As the book progresses, the chapters graduate from elementary symmetries and the invariance of algebraic equations, to ODEs and PDEs, followed by coverage of the nonclassical method and compatibility. Symmetry Analysis of Differential Equations: An Introduction also features: Detailed, step-by-step examples to guide readers through the methods of symmetry analysis End-of-chapter exercises, varying from elementary to advanced, with select solutions to aid in the calculation of the presented algorithmic methods Symmetry Analysis of Differential Equations: An Introduction is an ideal textbook for upper-undergraduate and graduate-level courses in symmetry methods and applied mathematics. The book is also a useful reference for professionals in science, physics, and engineering, as well as anyone wishing to learn about the use of symmetry methods in solving differential equations.

Eubank Randall Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators Eubank Randall Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
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Eubank Randall Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators


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7418.91 руб.

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.

Feng Fu Advanced Modelling Techniques in Structural Design Feng Fu Advanced Modelling Techniques in Structural Design
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Feng Fu Advanced Modelling Techniques in Structural Design


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9040.97 руб.

The successful design and construction of iconic new buildings relies on a range of advanced technologies, in particular on advanced modelling techniques. In response to the increasingly complex buildings demanded by clients and architects, structural engineers have developed a range of sophisticated modelling software to carry out the necessary structural analysis and design work. Advanced Modelling Techniques in Structural Design introduces numerical analysis methods to both students and design practitioners. It illustrates the modelling techniques used to solve structural design problems, covering most of the issues that an engineer might face, including lateral stability design of tall buildings; earthquake; progressive collapse; fire, blast and vibration analysis; non-linear geometric analysis and buckling analysis . Resolution of these design problems are demonstrated using a range of prestigious projects around the world, including the Buji Khalifa; Willis Towers; Taipei 101; the Gherkin; Millennium Bridge; Millau viaduct and the Forth Bridge, illustrating the practical steps required to begin a modelling exercise and showing how to select appropriate software tools to address specific design problems.

Wei Sun Power System Control Under Cascading Failures. Understanding, Mitigation, and System Restoration Wei Sun Power System Control Under Cascading Failures. Understanding, Mitigation, and System Restoration
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Wei Sun Power System Control Under Cascading Failures. Understanding, Mitigation, and System Restoration


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13360.72 руб.

Offers a comprehensive introduction to the issues of control of power systems during cascading outages and restoration process Power System Control Under Cascading Failures offers comprehensive coverage of three major topics related to prevention of cascading power outages in a power transmission grid: modelling and analysis, system separation and power system restoration. The book examines modelling and analysis of cascading failures for reliable and efficient simulation and better understanding of important mechanisms, root causes and propagation patterns of failures and power outages. Second, it covers controlled system separation to mitigate cascading failures addressing key questions such as where, when and how to separate. Third, the text explores optimal system restoration from cascading power outages and blackouts by well-designed milestones, optimised procedures and emerging techniques. The authors – noted experts in the field – include state-of-the-art methods that are illustrated in detail as well as practical examples that show how to use them to address realistic problems and improve current practices. This important resource: Contains comprehensive coverage of a focused area of cascading power system outages, addressing modelling and analysis, system separation and power system restoration Offers a description of theoretical models to analyse outages, methods to identify control actions to prevent propagation of outages and restore the system Suggests state-of-the-art methods that are illustrated in detail with hands-on examples that address realistic problems to help improve current practices Includes companion website with samples, codes and examples to support the text Written for postgraduate students, researchers, specialists, planners and operation engineers from industry, Power System Control Under Cascading Failures contains a review of a focused area of cascading power system outages, addresses modelling and analysis, system separation, and power system restoration.

Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics
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Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics


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4750.48 руб.

A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning.

Robert Hirsch P. Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel Robert Hirsch P. Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel
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Robert Hirsch P. Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel


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9040.97 руб.

A practical and methodological approach to the statistical logic of biostatistics in the field of health research Focusing on a basic understanding of the methods and analyses in health research, Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel® provides statistical concepts for interpreting results using Excel. The book emphasizes the application of methods and presents the most common methodological procedures in health research, which includes multiple regression, ANOVA, ANCOVA, logistic regression, Cox regression, stratified analysis, life table analysis, and nonparametric parallels. The book is constructed around a flowchart that outlines the appropriate circumstances for selecting a method to analyze a specific set of data. Beginning with an introduction to the foundational methods of statistical logic before moving on to more complex methods, Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel® also includes: Detailed discussions of how knowledge and skills in health research have been integrated with biostatistical methods Numerous examples with clear explanations that use mostly real-world health research data in order to provide a better understanding of the practical applications Implements Excel graphic representations throughout to help readers evaluate and analyze individual results An appendix with basic information on how to use Excel A companion website with additional Excel files, data sets, and homework problems as well as an Instructor’s Solutions Manual Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel® is an excellent textbook for upper-undergraduate and graduate-level courses in biostatistics and public health. In addition, the book is an appropriate reference for both health researchers and professionals.

Raymond Myers H. Generalized Linear Models. with Applications in Engineering and the Sciences Raymond Myers H. Generalized Linear Models. with Applications in Engineering and the Sciences
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Raymond Myers H. Generalized Linear Models. with Applications in Engineering and the Sciences


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11138.76 руб.

Praise for the First Edition «The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.» —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

Jill Fiedler-Kelly Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models Jill Fiedler-Kelly Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models
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Jill Fiedler-Kelly Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models


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8245.64 руб.

This book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis. • Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects • Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment • Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control • Has datasets, programming code, and practice exercises with solutions, available on a supplementary website

Cristina Davino Quantile Regression. Theory and Applications Cristina Davino Quantile Regression. Theory and Applications
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Cristina Davino Quantile Regression. Theory and Applications


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7735.78 руб.

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.


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Praise for the First Edition «The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.» —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.
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