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Applying Generalized Linear Models

  • Kartonierter Einband
  • 276 Seiten
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This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled i... Weiterlesen
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This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Generalized linear models have applications in many areas, including social science and life science. This book will serve as a reference and advanced text for students interested in the applications of statistics.

From a review:
"...one of the best applied statistical books that I have ever read."


Generalized Linear Modelling: Statistical Modelling.- Exponential Dispersion Models.- Linear Structure.- Three Components of a GLM.- Possible Models.- Inference.- Exercises. Discrete Data: Log Linear Models.- Models of Change.- Overdispersion.- Exercises. Fitting and Comparing Probability Distributions: Fitting Distributions.- Setting Up the Model.- Special Cases.- Exercises. Growth Curves: Exponential Growth Curves.- Logistic Growth Curve.- Gomperz Growth Curve.- More Complex Models.- Exercises. Time Series: Poisson Processes.- Markov Processes.- Repeated Measurements.- Exercises. Survival Data: General Concepts.- "Nonparametric" Estimation.- Parametric Models.- "Semiparametric" Models.- Exercises. Event Histories: Event Histories and Survival Distributions.- Counting processes.- Modelling Event Histories.- Generalizations.- Exercises. Spatial data: Spatial Interaction.- Spatial Patterns.- Exercises. Normal Models: Linear Regression.- Analysis of Variance.- Nonlinear Regression.- Exercises. Dynamic Models: Dynamic Generalized Linear Models.- Normal Models.- Count Data.- Positive Response Data.- Continuous Time Nonlinear Models. Appendices: Inference.- Diagnostics.- References.- Index.


Titel: Applying Generalized Linear Models
EAN: 9781475771114
ISBN: 1475771118
Format: Kartonierter Einband
Herausgeber: Springer New York
Genre: Mathematik
Anzahl Seiten: 276
Gewicht: 423g
Größe: H235mm x B155mm x T14mm
Jahr: 2013
Auflage: Softcover reprint of the original 1st ed. 1997

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