6 edition of **An introduction to generalized linear models** found in the catalog.

An introduction to generalized linear models

Annette J. Dobson

- 281 Want to read
- 2 Currently reading

Published
**2008**
by CRC Press in Boca Raton
.

Written in English

- Linear models (Statistics)

**Edition Notes**

Includes bibliographical references and index.

Statement | Annette J. Dobson and Adrian G Barnett. |

Series | Chapman & Hall/CRC texts in statistical science series -- 77 |

Contributions | Barnett, Adrian G. |

Classifications | |
---|---|

LC Classifications | QA276 .D589 2008 |

The Physical Object | |

Pagination | p. cm. |

ID Numbers | |

Open Library | OL23156225M |

ISBN 10 | 9781584889502 |

LC Control Number | 2008013034 |

Survival analysis, as the name implies, involves developing regression models for predicting how long an individual or object survives until an event, such as death in the case of an individual or failure in the case of an object such as a machine part, occurs. Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the U.S. Republican.

Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models.4/5(21). Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related ghly revised and updated, An 5/5(2). The Poisson regression model assumes that the random component of the regression model has a specific probability distribution that in this case is the Poisson distribution. The Poisson distribution is applicable to count data.

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This book provides readers with basic modeling principles that are applicable to a wide variety of situations. Key Features: Provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation - Includes discussion on checking model adequacy and description on how to use SAS to fit GLM.

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian by: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods.

This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical by: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods.

This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.

An Introduction to Generalized Linear Models (Quantitative Applications in the Social Sciences Book ) 1st Edition, Kindle Edition by George Henry Dunteman (Author), Moon-Ho R.

Ho (Author) Format: Kindle Edition out of 5 stars 4 ratings/5(4). An Introduction to Generalized Linear Models, Fourth Editionprovides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.

This item: INTRODUCTION TO GENERALIZED LINEAR MODELS, 3RD EDITION [Hardcover] [ ] DOBSON J. ANNETTE by DOBSON J. ANNETTE. Hardcover $ Introduction to Probability Models by Sheldon M.

Ross Hardcover $ Introductory Time Series with R (Use R!) by Paul S.P. Cowpertwait Paperback $/5(1). Introduction Generalized Linear Models Structure Generalized Linear Models (GLMs) A generalized linear model is made up of a linear predictor i = 0 + 1 x 1 i ++ p x pi and two functions I a link function that describes how the mean, E (Y i) = i, depends on the linear predictor g(i) = i I a variance function that describes how the variance, var(Y i).

Synopsis Popular for its accessible, concise, and clear introduction to this key statistical methodology, "An Introduction to Generalized Linear Models, Third Edition" provides a wealth of examples from such diverse fields as business, medicine, engineering, and the social sciences/5(23).

Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models.4/5.

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Book Description. Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis.

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By Annette J Dobson, Adrian G Barnett. Edition 3rd Edition. First Published eBook Published 12 May Pub. location New by: An Introduction to Generalized Linear Models (Quantitative Applications in the Social Sciences series) by George Henry Dunteman. Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM).

An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and Cited by: Gender Equality in Education in Post-Conflict Countries: Using Linear Regression Models to Advocate for New Policy Frameworks Introducing Multilevel Modeling Learn About Multiple Regression in Stata With Data From the Eurobarometer (, Jan–Feb ).

An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods.

This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Editionprovides a cohesive framework for. An Introduction to Generalized Linear Models (3rd ed.) (Chapman & Hall/CRC Texts in Statistical Science series) by Annette J.

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Dobson, Adrian G. Barnett | download | B–OK. Download books for free. Find books.Summary: "An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling." "This edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data.