# Search Results for "foundations-of-linear-and-generalized-linear-models"

## Foundations of Linear and Generalized Linear Models

**Author**: Alan Agresti**Publisher:**John Wiley & Sons**ISBN:**1118730038**Category:**Mathematics**Page:**444**View:**2477

"This book presents an overview of the foundations and the key ideas and results of linear and generalized linear models under one cover. Written by a prolific academic, researcher, and textbook writer, Foundations of Linear and Generalized Linear Modelsis soon to become the gold standard by which all existing textbooks on the topic will be compared. While the emphasis is clearly and succinctly on theoretical underpinnings, applications in "R" are presented when they help to elucidate the content or promote practical model building. Each chapter contains approximately 15-20 exercises, primarily for readers to practice and extend the theory, but, also to assimilate the ideas by doing some data analysis. The carefully crafted models and examples convey basic concepts and do not get mired down in non-trivial considerations. An author-maintained web site includes, among other numerous pedagogical supplements, analyses that parallel the "R" routines from the book in SAS, SPSS and Stata"--

## An Introduction to Categorical Data Analysis

**Author**: Alan Agresti**Publisher:**Wiley**ISBN:**1119405262**Category:**Mathematics**Page:**400**View:**3194

A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.

## Multivariate Density Estimation

*Theory, Practice, and Visualization*

**Author**: David W. Scott**Publisher:**John Wiley & Sons**ISBN:**1118575482**Category:**Mathematics**Page:**384**View:**2222

Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.

## 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:**3568

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.

## Foundations of Statistical Analyses and Applications with SAS

**Author**: Michael Falk,Frank Marohn,Bernward Tewes**Publisher:**Springer Science & Business Media**ISBN:**9783764368937**Category:**Mathematics**Page:**402**View:**6274

This book links up the theory of a selection of statistical procedures used in general practice with their application to real world data sets using the statistical software package SAS (Statistical Analysis System). These applications are intended to illustrate the theory and to provide, simultaneously, the ability to use the knowledge effectively and readily in execution.

## Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques

**Author**: Edward W. Frees,Richard A. Derrig,Glenn Meyers**Publisher:**Cambridge University Press**ISBN:**1139992317**Category:**Business & Economics**Page:**N.A**View:**1412

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.

## Predictive Modeling Applications in Actuarial Science

**Author**: Edward W. Frees,Richard A. Derrig,Glenn Meyers**Publisher:**Cambridge University Press**ISBN:**1107029872**Category:**Business & Economics**Page:**544**View:**8089

This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

## Foundations of Statistical Inference

*Proceedings of the Shoresh Conference 2000*

**Author**: Yoel Haitovsky,Hans Rudolf Lerche,Ya'acov Ritov**Publisher:**Springer Science & Business Media**ISBN:**3642574106**Category:**Mathematics**Page:**230**View:**4574

This volume is a collection of papers presented at a conference held in Shoresh Holiday Resort near Jerusalem, Israel, in December 2000 organized by the Israeli Ministry of Science, Culture and Sport. The theme of the conference was "Foundation of Statistical Inference: Applications in the Medical and Social Sciences and in Industry and the Interface of Computer Sciences". The following is a quotation from the Program and Abstract booklet of the conference. "Over the past several decades, the field of statistics has seen tremendous growth and development in theory and methodology. At the same time, the advent of computers has facilitated the use of modern statistics in all branches of science, making statistics even more interdisciplinary than in the past; statistics, thus, has become strongly rooted in all empirical research in the medical, social, and engineering sciences. The abundance of computer programs and the variety of methods available to users brought to light the critical issues of choosing models and, given a data set, the methods most suitable for its analysis. Mathematical statisticians have devoted a great deal of effort to studying the appropriateness of models for various types of data, and defining the conditions under which a particular method work. " In 1985 an international conference with a similar title* was held in Is rael. It provided a platform for a formal debate between the two main schools of thought in Statistics, the Bayesian, and the Frequentists.

## Finite-Elemente-Methoden

**Author**: Klaus-Jürgen Bathe**Publisher:**Springer Verlag**ISBN:**9783540668060**Category:**Technology & Engineering**Page:**1253**View:**3811

Dieses Lehr- und Handbuch behandelt sowohl die elementaren Konzepte als auch die fortgeschrittenen und zukunftsweisenden linearen und nichtlinearen FE-Methoden in Statik, Dynamik, Festkörper- und Fluidmechanik. Es wird sowohl der physikalische als auch der mathematische Hintergrund der Prozeduren ausführlich und verständlich beschrieben. Das Werk enthält eine Vielzahl von ausgearbeiteten Beispielen, Rechnerübungen und Programmlisten. Als Übersetzung eines erfolgreichen amerikanischen Lehrbuchs hat es sich in zwei Auflagen auch bei den deutschsprachigen Ingenieuren etabliert. Die umfangreichen Änderungen gegenüber der Vorauflage innerhalb aller Kapitel - vor allem aber der fortgeschrittenen - spiegeln die rasche Entwicklung innerhalb des letzten Jahrzehnts auf diesem Gebiet wieder.

## Mathematical and Numerical Foundations of Turbulence Models and Applications

**Author**: Tomás Chacón Rebollo,Roger Lewandowski**Publisher:**Springer**ISBN:**1493904558**Category:**Mathematics**Page:**517**View:**471

With applications to climate, technology, and industry, the modeling and numerical simulation of turbulent flows are rich with history and modern relevance. The complexity of the problems that arise in the study of turbulence requires tools from various scientific disciplines, including mathematics, physics, engineering and computer science. Authored by two experts in the area with a long history of collaboration, this monograph provides a current, detailed look at several turbulence models from both the theoretical and numerical perspectives. The k-epsilon, large-eddy simulation and other models are rigorously derived and their performance is analyzed using benchmark simulations for real-world turbulent flows. Mathematical and Numerical Foundations of Turbulence Models and Applications is an ideal reference for students in applied mathematics and engineering, as well as researchers in mathematical and numerical fluid dynamics. It is also a valuable resource for advanced graduate students in fluid dynamics, engineers, physical oceanographers, meteorologists and climatologists.

## Analytical Foundations of Marxian Economic Theory

**Author**: John E. Roemer**Publisher:**Cambridge University Press**ISBN:**9780521347754**Category:**Business & Economics**Page:**220**View:**369

This book gives a rigorous view of classical Marxian economic theory by presenting specific analytic models.

## Applied Logistic Regression

**Author**: David W. Hosmer, Jr.,Stanley Lemeshow,Rodney X. Sturdivant**Publisher:**John Wiley & Sons**ISBN:**1118548353**Category:**Mathematics**Page:**528**View:**7306

A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

## Foundations of Intelligent Systems

*16th International Symposium, ISMIS 2006, Bari, Italy, September 27-29, 2006, Proceedings*

**Author**: Floriana Esposito,Zbigniew W Ras,Donato Malerba,Giovanni Semeraro**Publisher:**Springer Science & Business Media**ISBN:**354045764X**Category:**Computers**Page:**770**View:**2676

This book constitutes the refereed proceedings of the 16th International Symposium on Methodologies for Intelligent Systems, ISMIS 2006. The book presents 81 revised papers together with 3 invited papers. Topical sections include active media human-computer interaction, computational intelligence, intelligent agent technology, intelligent information retrieval, intelligent information systems, knowledge representation and integration, knowledge discovery and data mining, logic for AI and logic programming, machine learning, text mining, and Web intelligence.

## Foundations of Time Series Analysis and Prediction Theory

**Author**: Mohsen Pourahmadi**Publisher:**John Wiley & Sons**ISBN:**9780471394341**Category:**Mathematics**Page:**414**View:**2122

The author emphasizes the foundation and structure of time series and backs up this coverage with theory and application.".

## Foundations of Computing and Decision Sciences

**Author**: N.A**Publisher:**N.A**ISBN:**N.A**Category:**Computer science**Page:**N.A**View:**9105

All subareas of Computing and Decision Sciences, in particular: computational complexity theory, parallel computations, combinatorics, consistency control, scheduling theory, distributed algorithms and systems, networking, databases, artificial intellingence (especially knowledge engineering, reasoning and learning models, search methods, speech recognition and synthesis), human-computer communication), ... decision analysis, ... decision support systems, production and project scheduling.

## 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:**6355

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

## Robust Methods in Biostatistics

**Author**: Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser**Publisher:**John Wiley & Sons**ISBN:**9780470740545**Category:**Medical**Page:**292**View:**1218

Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

## Intelligent Data Analysis

*An Introduction*

**Author**: Michael Berthold,Michael R. Berthold,David J. Hand**Publisher:**Springer Science & Business Media**ISBN:**9783540430605**Category:**Computers**Page:**514**View:**6665

This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.

## General Equilibrium Foundations of Finance

*Structure of Incomplete Markets Models*

**Author**: Thorsten Hens,Beate Pilgrim**Publisher:**Springer Science & Business Media**ISBN:**9781402073373**Category:**Business & Economics**Page:**299**View:**2688

The purpose of General Equilibrium Foundations of Finance is to give a sound economic foundation of finance based on the general equilibrium model with incomplete markets which embodies the famous CAPM as an important special case. This goal is achieved by giving reasonable restrictions on the agents' characteristics that lead to a well determined financial markets model having a unique competitive equilibrium. The innovation of this book is to transfer and to extend the theoretical results on the structure of competitive equilibria into the modern context of incomplete financial markets. General Equilibrium Foundations of Finance should be easily accessible by advanced Ph.D. students as well as by theorists of any subfield of mathematical economics. It should be interesting both for theorists who are looking for possible applications of rigorous theorizing as well as for practitioners who seek for a theoretical foundation of fruitful applications of financial markets' models.

## FSTTCS 2005: Foundations of Software Technology and Theoretical Computer Science

*25th International Conference, Hyderabad, India, December 15-18, 2005, Proceedings*

**Author**: R. Ramanujam**Publisher:**Springer Science & Business Media**ISBN:**3540304959**Category:**Computers**Page:**566**View:**6485

This book constitutes the refereed proceedings of the 25th International Conference on the Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2005, held in Hyderabad, India, in December 2005. The 38 revised full papers presented together with 7 invited papers were carefully reviewed and selected from 167 submissions. A broad variety of current topics from the theory of computing are addressed, ranging from software science, programming theory, systems design and analysis, formal methods, mathematical logic, mathematical foundations, discrete mathematics, combinatorial mathematics, complexity theory, and automata theory to theoretical computer science in general.