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Abstract

Classical statistical models for regression, time series, and longitudinal data analysis are generally useful in situations where data are approximately Gaussian and can be explained by some linear structure. These models are easy to interpret and the methods are theoretically well understood and investigated. However, the underlying assumptions may be too stringent and applications of the methods may be misleading in situations where data are clearly non-normal, such as categorical or counted data. Statistical modelling aims at providing more flexible model-based tools for data analysis.

Keywords

Generalize Linear Model Hide Markov Model Markov Chain Model Caesarian Section Categorical Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Ludwig Fahrmeir
    • 1
  • Gerhard Tutz
    • 2
  1. 1.Department of StatisticsUniversity of MunichMünchenGermany
  2. 2.Department of StatisticsUniversity of MunichMünchenGermany

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