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Statistics & Computing Books

You are currently browsing 131–140 of 166 new and published books in the subject of Statistics & Computing — sorted by publish date from newer books to older books.

For books that are not yet published; please browse forthcoming books.

New and Published Books – Page 14

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Correspondence Analysis and Data Coding with Java and R

    By Fionn Murtagh

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

    Developed by Jean-Paul Benzérci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of...

    Published May 25th 2005 by Chapman and Hall/CRC

  9. Sharpening Your SAS Skills

    By Sunil Gupta, Curt Edmonds

    Learn how to read, understand, and write better SAS programs¨ Understand the key differences between similar SAS syntax and programming approaches¨ Save time in writing SAS code with organized summaries of important facts¨ Improve your trouble-shooting skills in common programming and data related...

    Published April 28th 2005 by Chapman and Hall/CRC

  10. Gaussian Markov Random Fields

    Theory and Applications

    By Havard Rue, Leonhard Held

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

    Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the...

    Published February 17th 2005 by Chapman and Hall/CRC

Forthcoming Books

  1. Robust Cluster Analysis and Variable Selection
    By Gunter Ritter
    To Be Published September 3rd 2014
  2. Multiple Factor Analysis by Example Using R
    By Jérôme Pagès
    To Be Published September 21st 2014
  3. Regression Models for Categorical Dependent Variables Using Stata, Third Edition
    By J. Scott Long, Jeremy Freese
    To Be Published September 29th 2014
  4. Analyzing Sensory Data with R
    By Sebastien Le, Thierry Worch
    To Be Published September 30th 2014
  5. Advanced R
    By Hadley Wickham
    To Be Published October 2nd 2014

Find more forthcoming books