Numerical Issues in Statistical Computing for

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. Emphasizes specific numerical problems or specified statistical procedures as well as their applications (uniquely designed for this volume by key contributors in their respective fields); and much more!

MICAH ALTMAN is Associate Director of the Harvard-MIT Data Center in Cambridge, Massachusetts. <p>JEFF GILL is Associate Professor of Political Science at the University of California, Davis. <p>MICHAEL P. McDONALD is Assistant Professor of Government and Politics at George Mason University in Fairfax, Virginia.

Preface.

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.

Bibliography.

Author Index.

Subject Index.

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Numerical Issues in Statistical Computing for the Social Scientist

Summary

Author Biography

Table of Contents

Supplemental Materials