Handbook of Statistics 9: Computational Statistics

Edited by

  • C.R. Rao, Pennsylvania State University, University Park, PA, USA

The chapters in this volume, written by specialists in computer science and statistics, illustrate the trend in modern statistics of basic methodology supported by the state-of-the-art computational and graphical facilities, and their applications to diverse fields of human endeavour. Specifically, the Handbook is designed to serve as a practical guide to consulting statisticians; to provide research workers with an overview of current developments in computing and indicate their possible use in statistical work; to bring the latest developments in certain areas of computing and demands for the future to the attention of computer scientists; and to promote an interface between statisticians and computer scientists for the benefit of both.

This work is a valuable guide to computer scientists, statistical consultants, computer programmers and research workers in all fields involved in data analysis.

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Book information

  • Published: September 1993
  • Imprint: NORTH-HOLLAND
  • ISBN: 978-0-444-88096-3


... it provides several very good review articles

... a comprehensive and up-to-date overview of all numerical and computer related aspects of statistics. .. can be recommended to everyone who is interested in computer-based data analysis.
Statistical Software Newsletter
... the volume can be recommended for applied statisticians as well as researchers in the field of computational statistics.... For academic libraries this additional volume of the Handbook of Statistics is certainly a must.
Computational Statistics

Table of Contents

Computing - An Overview. Algorithms (B. Kalyanasundaram). Steady State Analysis of Stochastic Systems (K. Kant). Parallel Computer Architectures (R. Krishnamurti, B. Narahari). Database Systems (S. Lanka, S. Pal). Programming Languages and Systems (S. Purushothaman, J. Seaman). Algorithms and Complexity for Markov Processes (R. Varadarajan). Mathematical Programming and Applications to Statistics. Mathematical Programming: A Computational Perspective (W.W. Hager, R. Horst, P.M. Pardalos). Integer Programming (P.M. Pardalos, Y. Li). Least Squares Estimation. Numerical Aspects of Solving Linear Least Squares Problems (J.L. Barlow). The Total Least Squares Problem (S. Van Huffel, H. Zha). General Estimation Problems. Construction of Reliable Maximum-Likelihood-Algorithms with Applications to Logistic and Cox Regression (D. Böhning). Nonparametric Function Estimation (T. Gasser, J. Engel, B. Seifert). Computation Using the QR Decomposition (C.R. Goodall). The EM Algorithm (N. Laird). Analysis of Ordered Categorical Data Through Appropriate Scaling (C.R. Rao, P.M. Caligiuri). Artificial Intelligence and Statistics. Statistical Applications of Artificial Intelligence (W.A. Gale, D.J. Hand, A.E. Kelly). Some Aspects of Natural Language Processes (A.K. Joshi). Simulation and Resampling. Gibbs Sampling (S.F. Arnold). Bootstrap Methodology (G.J. Babu, C.R. Rao). The Art of Computer Generation of Random Variables (M.T. Boswell et al.). Jackknife Variance Estimation and Bias Reduction (S.D. Peddada). Statistical Graphics. Designing Effective Statistical Graphs (D.A. Burn). Graphical Methods for Linear Models (A.S. Hadi). Graphics for Time Series Analysis (H.J. Newton). Graphics as Visual Language (T. Selker, A. Appel). Statistical Graphics and Visualization (E.J. Wegman, D.B. Carr). Multivariate Statistical Visualization (F.W. Young, R.A. Faldowski, M.M. McFarlane). Graphical Methods for Process Control (T.L. Ziemer). Subject Index. Contents of Previous Volumes.