Numerical Issues in Statistical Computing for the Social Scientist / Edition 1

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This clear, accessible text provides a unified collection of works that guide empirical social scientists past the traps and mines of modern statistical computing. This is the first book to give social scientists modern-day tools, tricks, and advice on numerical methods in statistical computation, yet remains accessible through explanation and example. The book introduces the basic principles of numerical computation, outlines the optimization process, and provides tools for assessing the sensitivity of the subsequent results to problems in the data or with the model.

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Editorial Reviews

From the Publisher
"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." (Zentralblatt Math 1130, May 2008)

"Clarity of presentations is excellent. Applied statisticians and computer scientists will like this book and find it very useful." (Journal of Statistical Computation and Simulation, November 2005)

"[The authors] …have succeeded in providing...a good understanding of the potential pitfalls involved in the implementation of methodology computationally, and...good advice on dealing with the problems that can arise." (Statistics in Medical Research, June 2005)

“This book provides the researcher with an overview of the issues involved in the implementation and computation of common statistical procedures….” (Statistical Methods in Medical Research, Vol. 14, 2005)

"…this book is a good reference for social scientists that are involved in computational statistics." (Journal of Statistical Software, April 2005)

"…timely and interesting, and on the whole provides a good balance of theory, application, and computation." (Technometrics, May 2005)

"…an excellent text. It has the potential to be enormously influential across the social sciences…It should be required reading for everyone who performs statistical computing at the advanced level…" (Journal of the American Statistical Association, June 2005)

“…a compact guide to the voluminous literature on optimisation, numerical analysis, and computational statistics. This is no small achievement.” (Statistical Software Newsletter in Computational Statistics and Data Analysis)

"…a very important one for researchers, social scientists, and…graduate and post-graduate students in various disciplines..." (Computing, July 6, 2004)

"This comprehensive research and guidebook by Altman, Gill, and McDonald offers to social scientists modern tools and tricks previously lacking in other works.” (Choice, June 2004, Vol. 41 No. 10)

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Product Details

Meet the Author

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.

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Table of Contents


1. Introduction: Consequences of Numerical Inaccuracy.

2. Sources of Inaccuracy in Statistical Computation.

3. Evaluating Statistical Software.

4. Robust Inference.

5. Numerical Issues in Markov Chain Monte Carlo Estimation.

6. Numerical Issues Involved in Hessian Matrices (Jeff Gill & Gary King).

7. Numerical Behavior of King's EI Method.

8. Some Details of Nonlinear Estimation (B. D. McCullough).

9. Spatial Regression Models (James P. LeSage).

10. Convergence Problems in Logistic Regression (Paul Allison).

11. Recommendations for Replication and Accurate Analysis. 


Author Index.

Subject Index. 

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