Hi,

 

Hope this does not sound too ignorant .

 

I am trying to detrend and transform variables to achieve normality and
stationarity (for time series use, namely spectral analysis). I am using the
boxcox transformations. 

 

As my dataset contains zeros, I found I need to add a constant to it in
order to run "boxcox". I have ran tests adding several types of constants,
from .0001 (my unit of measurement) to 10 (still way below my maximum
value). Most of my data concentrates in low values. I found the estimate of
lambda changes drastically (from positive to negative) with the successive
constants. I also found that normality (evaluated by running Shapiro and
Jarque Bera test) after performing the suggested transformation obtained
from "box-cox" is maximized for constant=0.0001(pvalues>0.7, lambda=0.2) and
not for constant=1 (p-values <0.01, lambda=-0.94). Curiously, running the
Shapiro tests with constant=1 and across lambda values, the highest p-value
was obtained with lambda=0.2 (and not -0.94!)

 

Why does box-cox() return lambda values that are so far from "creating"
normality in the data? What type of best estimates are they? How should I
choose the constant? 

 

I have skimmed (I am not a statistician.) through the Box-Cox(1964) paper
and found no reference to this. 

 

So, any suggestion will be precious,

 

Nuno

 

 


	[[alternative HTML version deleted]]

