  The causes behind complications in laser-assisted tattoo removal are
currently not well understood, and in the literature relating to tattoo removal
the emphasis on removal treatment is on removal technologies and tools, not
best parameters involved in the treatment process. Additionally, the very
challenge of determining best practices is difficult given the complexity of
interactions between factors that may correlate to these complications. In this
paper we apply a battery of classical statistical methods and techniques to
identify features that may be closely correlated to causes of complication
during the tattoo removal process, and report quantitative evidence for
potential best practices. We develop elementary statistical descriptions of
tattoo data collected by the largest gang rehabilitation and reentry
organization in the world, Homeboy Industries; perform parametric and
nonparametric tests of significance; and finally, produce a statistical model
explaining treatment parameter interactions, as well as develop a ranking
system for treatment parameters utilizing bootstrapping and gradient boosting.
