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Statistical Models.

By: Material type: TextTextSeries: Publication details: Cambridge, U.K. ; New York : Cambridge University Press, 2003.Description: x, 726 p. : ill. ; 27 cmISBN:
  • 9780521734493
Subject(s): DDC classification:
  • 519.5   22
Contents:
1. Introduction; 2. Variation; 3. Uncertainty; 4. Likelihood; 5. Models; 6. Stochastic models; 7. Estimation and hypothesis testing; 8. Linear regression models; 9. Designed experiments; 10. Nonlinear regression models; 11. Bayesian models; 12. Conditional and marginal inference.
Summary: Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Main library General Stacks 519.5 / DA.S 2009 (Browse shelf(Opens below)) 1 Available 006753

Includes bibliographical references (p. 699-711) and indexes.

1. Introduction; 2. Variation; 3. Uncertainty; 4. Likelihood; 5. Models; 6. Stochastic models; 7. Estimation and hypothesis testing; 8. Linear regression models; 9. Designed experiments; 10. Nonlinear regression models; 11. Bayesian models; 12. Conditional and marginal inference.

Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.

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