Search Results for "applying-generalized-linear-models"

Applying Generalized Linear Models

Applying Generalized Linear Models

  • Author: James K. Lindsey
  • Publisher: Springer Science & Business Media
  • ISBN: 038722730X
  • Category: Mathematics
  • Page: 256
  • View: 3046
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This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Wahrscheinlichkeitstheorie und Stochastische Prozesse

Wahrscheinlichkeitstheorie und Stochastische Prozesse

  • Author: Michael Mürmann
  • Publisher: Springer-Verlag
  • ISBN: 364238160X
  • Category: Mathematics
  • Page: 428
  • View: 1394
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Dieses Lehrbuch beschäftigt sich mit den zentralen Gebieten einer maßtheoretisch orientierten Wahrscheinlichkeitstheorie im Umfang einer zweisemestrigen Vorlesung. Nach den Grundlagen werden Grenzwertsätze und schwache Konvergenz behandelt. Es folgt die Darstellung und Betrachtung der stochastischen Abhängigkeit durch die bedingte Erwartung, die mit der Radon-Nikodym-Ableitung realisiert wird. Sie wird angewandt auf die Theorie der stochastischen Prozesse, die nach der allgemeinen Konstruktion aus der Untersuchung von Martingalen und Markov-Prozessen besteht. Neu in einem Lehrbuch über allgemeine Wahrscheinlichkeitstheorie ist eine Einführung in die stochastische Analysis von Semimartingalen auf der Grundlage einer geeigneten Stetigkeitsbedingung mit Anwendungen auf die Theorie der Finanzmärkte. Das Buch enthält zahlreiche Übungen, teilweise mit Lösungen. Neben der Theorie vertiefen Anmerkungen, besonders zu mathematischen Modellen für Phänomene der Realität, das Verständnis.​

Generalized Linear Models

Generalized Linear Models

An Applied Approach

  • Author: John Patrick Hoffmann
  • Publisher: Pearson College Division
  • ISBN: N.A
  • Category: Mathematics
  • Page: 204
  • View: 8658
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This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.

Generalized Linear Models and Extensions, Second Edition

Generalized Linear Models and Extensions, Second Edition

  • Author: James William Hardin,Joseph M. Hilbe,Joseph Hilbe
  • Publisher: Stata Press
  • ISBN: 1597180149
  • Category: Computers
  • Page: 387
  • View: 9098
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Deftly balancing theory and application, this book stands out in its coverage of the derivation of the GLM families and their foremost links. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, and more.

Generalized Linear Models

Generalized Linear Models

with Applications in Engineering and the Sciences

  • Author: Raymond H. Myers,Douglas C. Montgomery,G. Geoffrey Vining,Timothy J. Robinson
  • Publisher: John Wiley & Sons
  • ISBN: 0470556978
  • Category: Mathematics
  • Page: 544
  • View: 7187
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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.

Introduction to General and Generalized Linear Models

Introduction to General and Generalized Linear Models

  • Author: Henrik Madsen,Poul Thyregod
  • Publisher: CRC Press
  • ISBN: 1439891141
  • Category: Mathematics
  • Page: 316
  • View: 7106
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Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R. After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at www.imm.dtu.dk/~hm/GLM

An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models

  • Author: George H. Dunteman,Moon-Ho R. Ho,Moon-Ho R.. Ho
  • Publisher: SAGE
  • ISBN: 9780761920847
  • Category: Mathematics
  • Page: 72
  • View: 5321
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Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Generalized Linear Models for Insurance Data

Generalized Linear Models for Insurance Data

  • Author: N.A
  • Publisher: Cambridge University Press
  • ISBN: N.A
  • Category:
  • Page: N.A
  • View: 1158
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Generalized Linear Mixed Models

Generalized Linear Mixed Models

  • Author: Charles E. McCulloch
  • Publisher: IMS
  • ISBN: 9780940600546
  • Category: Mathematics
  • Page: 84
  • View: 7436
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Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models

Generalized Linear Models

Generalized Linear Models

A Unified Approach

  • Author: Jeff Gill
  • Publisher: SAGE Publications
  • ISBN: 1506320244
  • Category: Social Science
  • Page: 112
  • View: 6517
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The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes his Web address where additional resources can be found.

An Introduction to Generalized Linear Models, Second Edition

An Introduction to Generalized Linear Models, Second Edition

  • Author: Annette J. Dobson
  • Publisher: CRC Press
  • ISBN: 9781420057683
  • Category: Mathematics
  • Page: 240
  • View: 999
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Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models. Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application. Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.

The Multivariate Social Scientist

The Multivariate Social Scientist

Introductory Statistics Using Generalized Linear Models

  • Author: Graeme D Hutcheson,Nick Sofroniou
  • Publisher: SAGE
  • ISBN: 9780761952015
  • Category: Social Science
  • Page: 276
  • View: 366
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Starting from simple hypothesis testing and then moving towards model-building, this valuable book takes readers through the basics of multivariate analysis including: which tests to use on which data; how to run analyses in SPSS for Windows and GLIM4; how to interpret results; and how to report and present the reports appropriately. Using a unified conceptual framework (based around the Generalized Linear Model) the authors explain the commonalities and relationships between methods that include both the analysis of categorical and continuous data.

The Multivariate Social Scientist

The Multivariate Social Scientist

Introductory Statistics Using Generalized Linear Models

  • Author: Graeme D Hutcheson,Nick Sofroniou
  • Publisher: SAGE
  • ISBN: 1446229947
  • Category: Social Science
  • Page: 288
  • View: 5235
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Starting from simple hypothesis testing and then moving towards model-building, this valuable book takes readers through the basics of multivariate analysis including: which tests to use on which data; how to run analyses in SPSS for Windows and GLIM4; how to interpret results; and how to report and present the reports appropriately. Using a unified conceptual framework (based around the Generalized Linear Model) the authors explain the commonalities and relationships between methods that include both the analysis of categorical and continuous data.

Das Versprechen

Das Versprechen

  • Author: Elena Boger
  • Publisher: Independently Published
  • ISBN: 9781720189787
  • Category:
  • Page: 279
  • View: 2156
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Eine Braut verschwindet von ihrer Hochzeitsfeier, die fieberhafte Suche nach ihr beginnt. Der Kommissar Preperin geht Spuren nach, ein altes Tagebuch bringt ihn auf eine neue Fährte. Aber was haben die Juwelen der letzten russischen Zarenfamilie mit dem Verschwinden der jungen Brat zu tun? Die Zeit wird knapp, es kann nur ein Wunder helfen.

Extending the Linear Model with R

Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

  • Author: Julian J. Faraway
  • Publisher: CRC Press
  • ISBN: 1498720986
  • Category: Mathematics
  • Page: 399
  • View: 696
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Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.

Modeling Techniques in Predictive Analytics with Python and R

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

  • Author: Thomas W. Miller
  • Publisher: FT Press
  • ISBN: 013389214X
  • Category: Computers
  • Page: 448
  • View: 5954
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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

Modeling Techniques in Predictive Analytics

Modeling Techniques in Predictive Analytics

Business Problems and Solutions with R, Revised and Expanded Edition

  • Author: Thomas W. Miller
  • Publisher: FT Press
  • ISBN: 0133886190
  • Category: Computers
  • Page: 384
  • View: 5769
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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

Regression

Regression

Modelle, Methoden und Anwendungen

  • Author: Ludwig Fahrmeir,Thomas Kneib,Stefan Lang
  • Publisher: Springer-Verlag
  • ISBN: 3642018378
  • Category: Business & Economics
  • Page: 502
  • View: 9159
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In dem Band beschreiben die Autoren erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten und anwendungsorientierten Form. Um Lesern die Analyse eigener Fragestellungen zu ermöglichen, demonstrieren sie die praktische Anwendung der Konzepte und Methoden anhand ausführlicher Fallstudien. Geeignet für Studierende der Statistik sowie für Wissenschaftler und Praktiker, zum Beispiel in den Wirtschafts- und Sozialwissenschaften, der Bioinformatik und -statistik, Ökonometrie und Epidemiologie.