Skewness, Kurtosis and Box-Cox for Censored Data? I am currently working on a classification plus regression problem on a dataset with over 500 predictors. Each predictor has on average 15% in-range values (above a defined threshold of signal detection. The rest of the values are simply represented as "out-of-range". This represents a scenario of censored data.
I would like to know if it is highly important to bring my predictor distributions close to Normal distribution through Box-Cox transform before I can work on scaling and dimensionality reduction.    
Or, is the need for bringing the predictor distributions closer to Normality dependent on the machine learning model I will be applying? I know for linear models, it would be helpful but kind of confused for other models.
Please let me know if you need any other information. 
 A: Got these insightful comments on my problem from /u/DrLionelRaymond:    
"Without knowing more about what you're hoping to accomplish it's difficult to make any definitive recommendations. It's possible that treating these variables as dichotomous (in range/out of range) or using a nonparametric approach would be sufficient/more appropriate.
That said, if you have a need for keeping the data in its continuous form, then yes,, there are better ways of handling left censored data. Using single value imputation (such as absolute minimum value) is fine when the amount of missing data is low, but at 85%+ missing data using a single value is going to introduce massive amounts of bias. I'd also avoid using MLE imputation (more robust to higher amounts of missing data but performance drops quite a bit once the amount of missing data approaches 80%).
My recommendation would be to look into distribution or Bayesian based MI methods. Rubin has published extensively on the distribution based approach and Chen has done a lot of work on the Bayesian approach. 85% missingness is really pushing the limit of what is possible without introducing potentially significant amounts of bias but those methods will likely give you the best results. It's important to point out that when performing the imputation you want the imputed values match the underlying distribution. You can perform normalization transformations once the imputation is complete. I believe wifi receiver signal strength follows a Gaussian distribution but log normal distributions are frequently seen in environmental exposure and biomarker data (See some of Jay Lubin's papers for good examples of using MCMC's for distribution based MI in left censored environmental exposure data). Q-Q plots are useful for seeing if your imputed data conforms to whichever underlying distribution you believed to be correct.    
Without knowing more about your use case or your data it'd be impossible for me to suggest what type of feature engineering would result in increased model accuracy."
