Dynamic Linear Models With R

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Dynamic Linear Models With R

Author : Giovanni Petris
ISBN : 9780387772387
Genre : Mathematics
File Size : 86.28 MB
Format : PDF, Docs
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State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Category: Mathematics

Generalized Linear Models

Author : Dipak K. Dey
ISBN : 0824790340
Genre : Mathematics
File Size : 26.26 MB
Format : PDF, Docs
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This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.
Category: Mathematics

A First Course In Linear Model Theory

Author : Nalini Ravishanker
ISBN : 1584882476
Genre : Mathematics
File Size : 33.29 MB
Format : PDF, Kindle
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This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models A First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding. The authors' fresh approach, methodical presentation, wealth of examples, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.
Category: Mathematics

Richly Parameterized Linear Models

Author : James S. Hodges
ISBN : 9781439866849
Genre : Mathematics
File Size : 88.73 MB
Format : PDF
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A First Step toward a Unified Theory of Richly Parameterized Linear Models Using mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects. Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The author examines what is known and unknown about mixed linear models and identifies research opportunities. The first two parts of the book cover an existing syntax for unifying models with random effects. The text explains how richly parameterized models can be expressed as mixed linear models and analyzed using conventional and Bayesian methods. In the last two parts, the author discusses oddities that can arise when analyzing data using these models. He presents ways to detect problems and, when possible, shows how to mitigate or avoid them. The book adapts ideas from linear model theory and then goes beyond that theory by examining the information in the data about the mixed linear model’s covariance matrices. Each chapter ends with two sets of exercises. Conventional problems encourage readers to practice with the algebraic methods and open questions motivate readers to research further. Supporting materials, including datasets for most of the examples analyzed, are available on the author’s website.
Category: Mathematics

Modeling Techniques In Predictive Analytics

Author : Thomas W. Miller
ISBN : 9780133886191
Genre : Computers
File Size : 79.53 MB
Format : PDF, ePub
Download : 881
Read : 739

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Category: Computers

Recursive Models Of Dynamic Linear Economies

Author : Lars Peter Hansen
ISBN : 9780691180731
Genre : Business & Economics
File Size : 78.33 MB
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A common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these tools to create a class of econometrically tractable models of prices and quantities. They present examples from microeconomics, macroeconomics, and asset pricing. The models are cast in terms of a representative consumer. While Hansen and Sargent demonstrate the analytical benefits acquired when an analysis with a representative consumer is possible, they also characterize the restrictiveness of assumptions under which a representative household justifies a purely aggregative analysis. Hansen and Sargent unite economic theory with a workable econometrics while going beyond and beneath demand and supply curves for dynamic economies. They construct and apply competitive equilibria for a class of linear-quadratic-Gaussian dynamic economies with complete markets. Their book, based on the 2012 Gorman lectures, stresses heterogeneity, aggregation, and how a common structure unites what superficially appear to be diverse applications. An appendix describes MATLAB programs that apply to the book's calculations.
Category: Business & Economics

Modeling Techniques In Predictive Analytics With Python And R

Author : Thomas W. Miller
ISBN : 9780133892147
Genre : Computers
File Size : 49.3 MB
Format : PDF, Kindle
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Category: Computers

The Oxford Handbook Of Applied Bayesian Analysis

Author : Anthony O' Hagan
ISBN : 9780191613890
Genre : Mathematics
File Size : 29.45 MB
Format : PDF, ePub
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Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.
Category: Mathematics

Discrete Time Dynamic Models

Author : Ronald K. Pearson
ISBN : 0195352815
Genre : Mathematics
File Size : 71.79 MB
Format : PDF
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Read : 593

Fueled by advances in computer technology, model-based approaches to the control of industrial processes are now widespread. While there is an enormous literature on modeling, the difficult first step of selecting an appropriate model structure has received almost no attention. This book fills the gap, providing practical insight into model selection for chemical processes and emphasizing structures suitable for control system design.
Category: Mathematics

Dynamic Economics

Author : Gregory C. Chow
ISBN : 0195356314
Genre : Business & Economics
File Size : 45.41 MB
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Read : 840

This work provides a unified and simple treatment of dynamic economics using dynamic optimization as the main theme, and the method of Lagrange multipliers to solve dynamic economic problems. The author presents the optimization framework for dynamic economics in order that readers can understand the approach and use it as they see fit. Instead of using dynamic programming, the author chooses instead to use the method of Lagrange multipliers in the analysis of dynamic optimization because it is easier and more efficient than dynamic programming, and allows readers to understand the substance of dynamic economics better. The author treats a number of topics in economics, including economic growth, macroeconomics, microeconomics, finance and dynamic games. The book also teaches by examples, using concepts to solve simple problems; it then moves to general propositions.
Category: Business & Economics

Pem Fuel Cell Diagnostic Tools

Author : Haijiang Wang
ISBN : 9781439839195
Genre : Science
File Size : 55.66 MB
Format : PDF, ePub
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PEM Fuel Cell Diagnostic Tools presents various tools for diagnosing PEM fuel cells and stacks, including in situ and ex situ diagnostic tools, electrochemical techniques, and physical/chemical methods. The text outlines the principles, experimental implementation, data processing, and application of each technique, along with its capabilities and weaknesses. The book covers many diagnostics employed in the characterization and determination of fuel cell performance. It discusses commonly used conventional tools, such as cyclic voltammetry, electrochemical impedance spectroscopy, scanning electron microscopy, and transmission electron microscopy. It also examines special tools developed specifically for PEM fuel cells, including transparent cells, cathode discharge, and current mapping, as well as recent advanced tools for diagnosis, such as magnetic resonance imaging and atomic force microscopy. For clarity, the book splits these diagnostic methodologies into two parts—in situ and ex situ. To better understand the tools, PEM fuel cell testing is also discussed. Each self-contained chapter provides cross-references to other chapters. Written by international scientists active in PEM fuel cell research, this volume incorporates state-of-the-art technical advances in PEM fuel cell diagnosis. The diagnostic tools presented help readers to understand the physical and chemical phenomena involved in PEM fuel cells.
Category: Science

Political Analysis

Author : Walter R. Mebane
ISBN : 0472110748
Genre : Political Science
File Size : 90.22 MB
Format : PDF
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Discusses some of the latest developments in political methodology
Category: Political Science

Topics In Experimental Dynamic Substructuring Volume 2

Author : Randy Mayes
ISBN : 9781461465409
Genre : Science
File Size : 75.34 MB
Format : PDF
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Topics in Experimental Dynamics Substructuring, Volume 2: Proceedings of the 31st IMAC, A Conference and Exposition on Structural Dynamics, 2013, the second volume of seven from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Nonlinear Substructures SEM Substructures Wind Turbine Testbed – Blade Modeling & Correlation Substructure Methods SEM Substructures Wind Turbine Testbed Frequency Based Substructures Fixed Base Substructure Methods Substructure Methods SEM Substructures Wind Turbine Testbed Frequency Based Substructures Fixed Base Substructure Methods
Category: Science

Advances In Econometrics Volume 1

Author : Econometric Society. World Congress
ISBN : 0521467268
Genre : Business & Economics
File Size : 23.30 MB
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With its focus on econometrics, this volume contains key papers delivered at the Fifth World Congress in 1985.
Category: Business & Economics

Analysis Of Geometrically Non Linear Models For Contact With Dry Friction

Author : Florian Schmid
ISBN : 9783832520052
Genre :
File Size : 22.47 MB
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This work establishes a mathematical existence theory for solutionsof some quasi-static models in contact mechanics with dry friction.The models are finite dimensional and friction is modeled accordingto Coulomb's law. The main focus is on the geometric non-linearitywhich is due to the curved obstacle surface.
Category: