Methods and Models


Book Description

At present much of political science consists of a large body of formal mathematical work that remains largely unexplored empirically and an expanding use of sophisticated statistical techniques. While there are examples of noteworthy efforts to bridge the gap between these, there is still a need for much more cooperative work between formal theorists and empirical researchers in the discipline. This book explores how empirical analysis has, can, and should be used to evaluate formal models in political science. The book is intended to be a guide for active and future political scientists who are confronting the issues of empirical analysis with formal models in their work and as a basis for a needed dialogue between empirical and formal theoretical researchers in political science. These developments, if combined, are potentially a basis for a new revolution in political science.




Methods And Models In Statistics: In Honour Of Professor John Nelder, Frs


Book Description

John Nelder was one of the most influential statisticians of his generation, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage.




Methods and Models in Mathematical Programming


Book Description

This book focuses on mathematical modeling, describes the process of constructing and evaluating models, discusses the challenges and delicacies of the modeling process, and explicitly outlines the required rules and regulations so that the reader will be able to generalize and reuse concepts in other problems by relying on mathematical logic.Undergraduate and postgraduate students of different academic disciplines would find this book a suitable option preparing them for jobs and research fields requiring modeling techniques. Furthermore, this book can be used as a reference book for experts and practitioners requiring advanced skills of model building in their jobs.




Data Mining Methods and Models


Book Description

Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.




Mathematical Methods and Models for Economists


Book Description

A textbook for a first-year PhD course in mathematics for economists and a reference for graduate students in economics.




Spatial Econometrics: Methods and Models


Book Description

Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.




Models & Methods for Project Selection


Book Description

Models & Methods for Project Selection systematically examines in this book treatment the latest work in the field of project selection modeling. The models presented are drawn from mathematical programming, decision theory, and finance. These models are examined in two categorical streams: the management science stream and the financial model stream. The book describes the assumptions and limitations of each model and provides appropriate solution methodologies. Its organization follows three main themes: *Criteria for Choice: Chapters 1-3 investigate the effect of the choice of optimization criteria on the results of the portfolio optimization problem. *Risk and Uncertainty: Chapters 4-7 deal with uncertainty in the project selection problem. *Non-Linearity and Interdependence: These chapters deal with problems of non-linearity and interdependence as they arise in the project selection problem. Chapters 8, 9 and 10 present solution methodologies, which can be used to solve these most general project selection models.




Complex Models and Computational Methods in Statistics


Book Description

The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.




Methods and Models for Studying the Individual


Book Description

[Publisher-supplied data] An international cast of the top names in developmental research explore how researchers can use group data to understand individual patterns and pathways over time. Since the dominant statistical models and sampling methods do not accurately reflect and capture the way in which individuals change, but rather statistically treat the individual as an unchanging constant (e.g., as if a 2-year-oldÆs personality is identical to a 30-year-oldÆs). After an introduction to this methodological dilemma, the book shows how empirical procedures can be employed in a stepwise progression to permit a focus on individual children. The next chapter explains how individuals versus variables are not stable over time and how to adjust analysis to reflect this. This is followed by a discussion that outlines some of the key technical details and illustrates the choices that confront researchers in the application of these procedures to longitudinal data sets. Subsequent chapters cover such issues as the value of methodological focus on extreme groups, such as fearful and very exuberant children; ways to analyze personality changes over time, how to combine the strengths of variable-oriented and categorical procedures to achieve a focus on individuals; how to correlate retrospective reports with actual events, and how developmental methods can be brought into line with the study of individual pathways. Each chapter is followed by a commentary and discussion by Marian Radke Yarrow. Methods and Models for Studying the Individual offers readers a guide for evaluating the methodological merits of the different strategies for studying individuals.




Generalized Linear Mixed Models


Book Description

With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.