Box-Cox power transformation

The Box-Cox power transformation is a data transformation to achieve a better normality and to stabilize the variance. In MCRA, the transformation parameter $p$ in $(y^p-1)/p$ is determined by maximizing the log-likelihood function

$$l(p)=-\frac{n}{s}\log [\frac{1}{n}\sum _{i=1}^n(y_i^{(p)}-\overline{y^{(p)}}{)}^2]+(p-1)\sum _{i=1}^n\log y_i$$

where $i$ indexes the $n$ observations and

$$\overline{y^{(p)}}=\frac{1}{n}\sum _{i=1}^n{y}_i^{(p)}$$

is the average of the $y_i^{(p)}$, see Box and Cox (1964).