A BAYESIAN-APPROACH TO TRANSFORMATIONS TO NORMALITY
成果类型：
Article
署名作者：
PERICCHI, LR
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.2307/2335803
发表日期：
1981
页码：
3543
关键词：
摘要：
The analysis of transformation of observations in the linear model with normal errors proposed by Box and Cox (1964) is considered. A different choice of noninformative unnormed prior is advocated, which is not outcome dependent. This new selection of prior leads to a formal identity between likelihood and Bayesian inference, both for the estimation of the best transformation to normality and for the presence of homoscedasticity and additivity under this transformation. Extension to a related problem is mentioned.