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Informationen zum Autor MICAH ALTMAN is Associate Director of the Harvard-MIT Data Center in Cambridge, Massachusetts. JEFF GILL is Associate Professor of Political Science at the University of California, Davis. MICHAEL P. McDONALD is Assistant Professor of Government and Politics at George Mason University in Fairfax, Virginia. Klappentext At last--a social scientist's guide through the pitfalls of modern statistical computing Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing. Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field. Highlights include: A focus on problems occurring in maximum likelihood estimation Integrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB(R)) A guide to choosing accurate statistical packages Discussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysis Emphasis on specific numerical problems, statistical procedures, and their applications in the field Replications and re-analysis of published social science research, using innovative numerical methods Key numerical estimation issues along with the means of avoiding common pitfalls A related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computation Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field. Zusammenfassung Serving as a "bridge" to prepare social scientists and students for professional-level use of statistics! this volume outlines the main numerical estimations issues along with various means of avoiding specific common pitfalls. Inhaltsverzeichnis Preface xi 1 Introduction: Consequences of Numerical Inaccuracy 1 1.1 Importance of Understanding Computational Statistics 1 1.2 Brief History: Duhem to the Twenty-First Century 3 1.3 Motivating Example: Rare Events Counts Models 6 1.4 Preview of Findings 10 2 Sources of Inaccuracy in Statistical Computation 12 2.1 Introduction 12 2.1.1 Revealing Example: Computing the Coefficient Standard Deviation 12 2.1.2 Some Preliminary Conclusions 13 2.2 Fundamental Theoretical Concepts 15 2.2.1 Accuracy and Precision 15 2.2.2 Problems, Algorithms, and Implementations 15 2.3 Accuracy and Correct Inference 18 2.3.1 Brief Digression: Why Statistical Inference Is Harder in Practice Than It Appears 20 2.4 Sources of Implementation Errors 21 2.4.1 Bugs, Errors, and Annoyances 22 2.4.2 Computer Arithmetic 23 2.5 Algorithmic Limitations 29 2.5.1 Randomized Algorithms 30 2.5.2 Appro...
