can someone help explain the box and cox
statistical technique for predictor
transformations? What determines the lambda value and how does that influence the use of different functions to perform the transformation?

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Lambda should be the value that once applied most closely represents a normal distribution.

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There are 2 methods for calculating lambda, the “guerrero” and “loglik” method (reference is the BoxCox.lambda function in the forecast package). In Guerrero’s (1993) method lambda minimizes the coefficient of variation for subseries of data. In the “loglik” method, the value of lambda is chosen to maximize the profile log likelihood of a linear model fitted to data.

Oh I remember this from my college days.. y’all are actually using it?

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