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Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The authors enable a clear comparison between general and generalized linear models and cover Gaussian-based hierarchical models and hierarchical generalized linear models. They illustrate the methods with many real-world examples and use R throughout to solve…mehr

Produktbeschreibung
Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The authors enable a clear comparison between general and generalized linear models and cover Gaussian-based hierarchical models and hierarchical generalized linear models. They illustrate the methods with many real-world examples and use R throughout to solve the problems. Ancillaries are available on the book's website.

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Autorenporträt
Henrik Madsen is a professor in the Department of Informatics and Mathematical Modelling at the Technical University of Denmark in Lyngby. He has authored or coauthored more than 400 publications. Dr. Madsen has also led or participated in research projects involving wind power and energy load forecasting, financial forecasting and modeling, heat dynamics modeling, PK/PD modeling in drug development, data assimilation, zooneses modeling, and high performance and scientific computing.