A transform-both-sides modulus power model: an application in health care

Abstract

The Box-Cox transformation is suitable for stabilizing variance of the response variable and for inducing functional form flexibility in single-equation regression models. However, it becomes incapacitated if the data contain zero or negative values. This paper, using profitability data for US psychiatric hospitals, illustrates the capability of modulus power transformations to symmetrize the response variable distribution. Compared with the untransformed data model, the ML estimates of the modulus power model are found to be superior.