Abstract
In ordinary least squares multiple regression, the objective in fitting a model is to find the values of the unknown parameters that minimize the sum of squared errors of prediction. When the response variable is polytomous or is not observed completely, a more general objective to optimize is needed.
Keywords
- Maximum Likelihood Estimate
- Penalty Function
- Wald Statistic
- Bootstrap Distribution
- Observe Information Matrix
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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© 2001 Springer Science+Business Media New York
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Harrell, F.E. (2001). Overview of Maximum Likelihood Estimation. In: Regression Modeling Strategies. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3462-1_9
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DOI: https://doi.org/10.1007/978-1-4757-3462-1_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2918-1
Online ISBN: 978-1-4757-3462-1
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