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I'm starting an academic project about transformations in linear models? At the moment I'm listing all the transformations that there are to correct violations on the assumptions of the linear regression model: linearity, normality, heteroskedasticity and independent errors. Transformations like Box-Cox or exponential are already part of this list. What I would like to do is an overview on transformations and therefore I intend to include as many transformations as possible, but in most of the literature I mainly find information about the power family of transformations and not much more.

I'm wondering if anyone has experience in this topic. Any comments or advice would be greatly appreciated.

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  • $\begingroup$ The monograph Transformation and Weighting in Regression by Carroll and Ruppert may be helpful. $\endgroup$ – Zhanxiong Sep 12 '16 at 13:22

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