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Statistics & Computing Books

You are currently browsing 131–140 of 169 new and published books in the subject of Statistics & Computing — sorted by publish date from newer books to older books.

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New and Published Books – Page 14

  1. Univariate and Multivariate General Linear Models

    Theory and Applications with SAS, Second Edition

    By Kevin Kim, Neil Timm

    Series: Statistics: A Series of Textbooks and Monographs

    Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a...

    Published October 10th 2006 by Chapman and Hall/CRC

  2. An Introduction to Modern Econometrics Using Stata

    By Christopher F. Baum

    Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that...

    Published August 16th 2006 by Stata Press

  3. Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition

    By Bryan F.J. Manly

    Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis...

    Published August 14th 2006 by Chapman and Hall/CRC

  4. Generalized Linear Models with Random Effects

    Unified Analysis via H-likelihood

    By Youngjo Lee, John A. Nelder, Yudi Pawitan

    Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

    Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide...

    Published July 12th 2006 by Chapman and Hall/CRC

  5. C++ for Mathematicians

    An Introduction for Students and Professionals

    By Edward Scheinerman

    For problems that require extensive computation, a C++ program can race through billions of examples faster than most other computing choices. C++ enables mathematicians of virtually any discipline to create programs to meet their needs quickly, and is available on most computer systems at no cost....

    Published June 5th 2006 by CRC Press

  6. Markov Chain Monte Carlo

    Stochastic Simulation for Bayesian Inference, Second Edition

    By Dani Gamerman, Hedibert F. Lopes

    Series: Chapman & Hall/CRC Texts in Statistical Science

    While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new...

    Published May 9th 2006 by Chapman and Hall/CRC

  7. Handbook of Parallel Computing and Statistics

    Edited by Erricos John Kontoghiorghes

    Series: Statistics: A Series of Textbooks and Monographs

    Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many...

    Published December 20th 2005 by Chapman and Hall/CRC

  8. Extending the Linear Model with R

    Generalized Linear, Mixed Effects and Nonparametric Regression Models

    By Julian J. Faraway

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which...

    Published December 19th 2005 by Chapman and Hall/CRC

  9. Robust Statistical Methods with R

    By Jana Jureckova, Jan Picek

    Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated....

    Published November 28th 2005 by Chapman and Hall/CRC

  10. Design and Modeling for Computer Experiments

    By Kai-Tai Fang, Runze Li, Agus Sudjianto

    Series: Chapman & Hall/CRC Computer Science & Data Analysis

    Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific...

    Published October 13th 2005 by Chapman and Hall/CRC