Pitfalls of Rescaling Regression Models with Box-Cox Transformations
指出在Box-Cox模型中，将因变量除以样本几何平均数进行重新缩放存在三个问题：模型不等价、参数解释不清且标准误和置信区间渐近无效，仅零系数检验有效。
To facilitate maximum likelihood estimation for Box-Cox models, several authors have suggested dividing the dependent variable by its sample geometric mean. This paper points out previously unmentioned drawbacks of this 'recalling.' First, the 'resealed' model is not actually equivalent to the untransformed one, so that the procedure involves more than a unit change. Second, there is no clear interpretation of the parameters after such resealing. The authors suggest an interpretation but find that the usual formulas for standard errors and confidence intervals are not asymptotically valid. Only tests for zero coefficients are valid. Thirdly, the authors discuss the appropriate way of measuring elasticities in such models. Copyright 1994 by MIT Press.