Abstract
Chapter 2 dealt with aspects of modeling such as transformations of predictors, relaxing linearity assumptions, modeling interactions, and examining lack of fit. Chapter 3 dealt with missing data, focusing on utilization of incomplete predictor information. All of these areas are important in the overall scheme of model development, and they cannot be separated from what is to follow. In this chapter we concern ourselves with issues related to the whole model, with emphasis on deciding on the amount of complexity to allow in the model and on dealing with large numbers of predictors. The chapter concludes with three default modeling strategies depending on whether the goal is prediction, estimation, or hypothesis testing.
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© 2001 Springer Science+Business Media New York
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Harrell, F.E. (2001). Multivariable Modeling Strategies. In: Regression Modeling Strategies. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3462-1_4
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DOI: https://doi.org/10.1007/978-1-4757-3462-1_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2918-1
Online ISBN: 978-1-4757-3462-1
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