On the Choice of Prior Distribution for the Box-Cox Transformed Linear Model
本文讨论了Box-Cox变换线性模型中非信息先验分布依赖于数据的问题，提出了一族不依赖结果的先验，并分析了其后验性质。
The noninformative prior distribution for the parameters of the linear model transformed following Box & Cox (1964) has the non-Bayesian property of depending to some extent on the data. An alternative choice of prior which is not outcome-dependent was suggested by Pericchi (1981), but it is argued here that this prior has some undesirable features. An alternative family of non-outcome-dependent priors is suggested, leading to a noninformative prior which is closer in spirit to that proposed by Box & Cox. The posterior consequences of adopting this prior are fully explored, and an example discussed.