Abstract
The Box-Cox transformation is a well known family of
power transformations that brings a set of data into agreement
with the normality assumption of the residuals and hence the
response variable of a postulated model in regression analysis. In
this paper we use six different data sets to implement adaptive
maximum likelihood Box-Cox transformation parameter
estimation in regression analysis. In addition, we perform
random permutation and Monte-Carlo simulation to investigate
the performances of the adaptive method.
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